The DOI matches its Crossref record; 119 of the 119 references it cites were found in a registry or an index; none of the 41 sources its claims cite is checked for support yet.
Medical image data augmentation: techniques, comparisons and interpretations
Open accessAbstract
Designing deep learning based methods with medical images has always been an attractive area of research to assist clinicians in rapid examination and accurate diagnosis. Those methods need a large number of datasets including all variations in their training stages. On the other hand, medical images are always scarce due to several reasons, such as not enough patients for some diseases, patients do not want to allow their images to be used, lack of medical equipment or equipment, inability to obtain images that meet the desired criteria. This issue leads to bias in datasets, overfitting, and inaccurate results. Data augmentation is a common solution to overcome this issue and various augmentation techniques have been applied to different types of images in the literature. However, it is not clear which data augmentation technique provides more efficient results for which image type since different diseases are handled, different network architectures are used, and these architectures are trained and tested with different numbers of data sets in the literature. Therefore, in this work, the augmentation techniques used to improve performances of deep learning based diagnosis of the diseases in different organs (brain, lung, breast, and eye) from different imaging modalities (MR, CT, mammography, and fundoscopy) have been examined. Also, the most commonly used augmentation methods have been implemented, and their effectiveness in classifications with a deep network has been discussed based on quantitative performance evaluations. Experiments indicated that augmentation techniques should be chosen carefully according to image types.
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40 Not checked yet · 26 No basis given · 25 Own interpretation · 13 Own result · 6 Own method
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In the paper
“However, the databases have imbalanced class distributions.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Materials and methods > Classification
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In the paper
“Augmentation of patches or whole mammography images have usually been performed by scaling, noise addition, mirroring, shearing, and rotation techniques or using several combinations of them (Table 3).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
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In the paper
“On the other hand, the combined technique consisting of salt and pepper noise addition and shearing is the least appropriate approach (Table 8).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Experiments in this study indicated that the combined technique consisting of translation, shearing, and rotation is the most appropriate approach for the augmentation of breast mammography images in order to improve the classification of the images as normal, benign, and malignant.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Experiments indicated that augmentation techniques should be chosen carefully according to image types.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“On the other hand, augmentation only by shearing is the least efficient approach (Table 7).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
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In the paper
“Therefore, there are a total of 888 images extracted from 1018 cases.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Materials and methods > Materials
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In the paper
“It has been observed that usage of the augmented images obtained by the combination of rotation with shearing, and translation provides higher performance than the other augmentation techniques in the classifications of HGG and LGG cases from FLAIR types of brain images.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
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In the paper
“The best augmentation method for different imaging modalities showing different organs has been determined according to quantitative values.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Introduction
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In the paper
“It has been observed that usage of the augmented images obtained by the combination of translation and shearing techniques in the classification of images with nodules and without nodules provides the highest performance than the other augmentation methods.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
plaindirect- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 29
- Probably not a substantial claim. 54
- The quote only partly states this claim. 3
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In those applications, different network architectures, datasets and number of images have been used, and also their performances have been evaluated with different metrics.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The number of images in the categories is 974, 75, and 151, respectively.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Materials and methods > Materials
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“High performances have been obtained from those applications according to the reported results.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. The quote only partly states this claim.
None of the sources cited for this claim have been checked yet.
In the paper
“The BRaTS database contains 285 images including HGG (210 images) and LGG (75 images) cases.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Materials and methods > Materials
plainn = 285- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the database, there are 1018 CT scans which belong to 1010 people.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Materials and methods > Materials
plainn = 1,010- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“It contains 410 images, of which only 107 images show breast masses, and the total number of mass lesions is 115 since an image may contain more than one lesion.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Materials and methods > Materials
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In those applications, different network structures, data sets and number of images have been used, and also their performances have been evaluated with different metrics.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The number of benign and malignant masses is 52 and 63, respectively.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Materials and methods > Materials
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“High performances have been obtained from those applications according to the reported results.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Commonly used augmentation techniques with eye fundus images to boost the performance of deep learning based approaches are rotation, shearing, flipping, and translation (Table 4).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Eye fundus images have been provided from the Messidor database (Decencière et al. 2014) including 1200 images categorized into three classes (0-risk, 1-risk, and 2-risk) according to the diabetic macular edema risk level.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Materials and methods > Materials
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Thanks to the augmentation methods, deep network architectures’ learning and generalization ability can be improved, and the desired robustness property of the networks can be provided (Sect. 2).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Deep networks need training sets with many images covering all variations to achieve high generalization and robustness.
In the paper
“The chosen imaging modalities are widely used in medicine for many applications such as classification of brain tumors, lung nodules, and breast lesions (Meijering 2020).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Introduction
What the sources say
- Not checked yetcites Meijering 2020 source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“According to the segmentation or classification results presented in the literature, it is not possible to understand or compare the impacts or contributions of the used augmentation approaches on the results, and to decide the most appropriate augmentation method.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Data augmentation is a common solution to overcome this issue”
§ Medical image data augmentation: techniques, comparisons and interpretations > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
- Rests on an assumption: Medical image datasets are usually limited in size.
- Rests on an assumption: Scarcity and imbalance in medical image data cause overfitting, bias, and inaccurate results.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, it is not clear which data augmentation technique provides more efficient results for which image type since different diseases are handled, different network architectures are used, and these architectures are trained and tested with different numbers of data sets in the literature.”
- Also stated in: § Introduction · plain
“It is not clear which data augmentation technique provides more efficient results for which image type since different diseases are handled, different network architectures are used, and these architectures are trained and tested with different numbers of data sets in the literature (Sect. 2).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Abstract
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
- Also stated in: § Introduction · plain
None of the sources cited for this claim have been checked yet.
In the paper
“Therefore, translated images provide prevention of positional bias (Shorten and Khoshgoftaar 2019) and the models do not focus on properties in a single spatial location (Nalepa et al. 2019).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Commonly applied augmentation methods
What the sources say
- Not checked yetcites Shorten and Khoshgoftaar 2019 source
- Not checked yetcites Nalepa et al. 2019
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For instance, NeoVessel (NV)-like structures have been synthesized in a heuristic image augmentation (Ara´ujo et al. 2020) to improve detection of proliferative DR which is an advanced DR stage characterized by neovascularization.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
What the sources say
- Not checked yetcites Ara´ujo et al. 2020
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, refinement and segmentation of the lesion regions before the application of an augmentation method is the most preferred way to provide good performance from the detection and classification of the lesions.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
boosteddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, to evaluate the effectiveness of those augmentation methods in classifications from the four types of images, a classifier model has been designed and classifications with the augmented images for each augmentation method have been performed with the same classifier model.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Introduction
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Also, pixel intensity values have been used in some augmentation techniques since fundus images are colored images.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For example, in a study (Shyamalee and Meedeniya 2022), a combined method including rotation, shearing, zooming, flipping, and shifting has been applied for glaucoma identification.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
What the sources say
- Not checked yetcites Shyamalee and Meedeniya 2022
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For instance, contextual GAN, which generates new images by synthesizing lesions according to the context of surrounding tissue, has been applied to improve binary classification (as normal and malignant) by Wu et al. (2020b).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
What the sources say
- Not checked yetcites Wu et al. (2020b)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For instance, a set of augmentation methods (namely mirroring, zooming, and resizing) has been applied to improve automated classification of images into three categories (as normal, malignant, and benign) (Zeiser et al. 2020).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
What the sources say
- Not checked yetcites Zeiser et al. 2020
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Five evaluation metrics have been used, and the results provided from each classification have been evaluated with the same evaluation metrics.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Introduction
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“The reason to choose this architecture is to utilize its advantage in addressing the gradient vanishing issue and high learning ability (He et al. 2016).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Materials and methods > Classification
What the sources say
- Not checked yetcites He et al. 2016
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Those features are used in deep learning based methods being developed for different purposes like glaucoma identification, classification of diabetic retinopathy (DR), and identification of DR severity.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Eye fundus features such as neovascularization, hemorrhages/microaneurysms, and exudates are symptoms of eye diseases.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although mostly those techniques have been preferred in the literature, augmentation has also been provided by producing synthetic images. To generate synthetic images, Mixup method, which combines two randomly selected images and their labels, or modified Mixup, or GAN structures have been used (Table 1).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of brain MR images
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, converting a color image to its grayscale version should be performed carefully because this conversion reduces performance by up 3% (Chatfield et al. 2014).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Commonly applied augmentation methods
What the sources say
- Not checked yetcites Chatfield et al. 2014
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In another work (Shen et al. 2021), both contextual and deep convolutional GAN have been used to generate synthetic lesion images with margin and texture information and achieve improvement in the detection and segmentation of lesions.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
What the sources say
- Not checked yetcites Shen et al. 2021
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The authors have also applied a channel-wise vessel augmentation using morphological transformations.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
What the sources say
- Not checked yetcites Sun et al. 2021
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Also, several GAN models have been used to augment lung CT images by generating new synthetic images (Table 2).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of lung CT images
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In another work (Agustin et al. 2020), to improve the robustness of DR detection performance, image augmentation has been performed by zooming and a contrast enhancement process known as Contrast Limited Adaptive Histogram Equalization (CLAHE).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
What the sources say
- Not checked yetcites Agustin et al. 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, the safety of this augmentation technique depends on the rotation degree.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Commonly applied augmentation methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Image labels may not always be preserved if the degree of rotation increases.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, in this study, the most commonly used augmentation methods have been implemented using the same datasets.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Only GAN based augmentations have also been used.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“For instance, conditional GAN (Zhou et al. 2020) and deep convolutional GAN (Balasubramanian et al. 2020) based augmentations have been used to increase the performance of the methods developed for automatic classifications of DR according to its severity level.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
What the sources say
- Not checked yetcites Zhou et al. 2020
- Not checked yetcites Balasubramanian et al. 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“To overcome those issues, several GAN variants were developed such as conditional, deep convolutional, and cycle GAN.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Commonly applied augmentation methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The main challenging issues of GAN-based image augmentation are generating high-quality images and maintaining training stability.
In the paper
“For instance, to achieve robust segmentation of retinal vessels, augmentation has been provided by a channel-wise random gamma correction (Sun et al. 2021).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
What the sources say
- Not checked yetcites Sun et al. 2021
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Also, those techniques have been used together with GANs to provide augmentations with new synthetic images.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For instance, flipping and deep convolutional GAN has been combined for binary classifications of images (as normal and with a mass) by Alyafi et al. (2020).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
What the sources say
- Not checked yetcites Alyafi et al. (2020)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“In some works, retinal features (such as lesion and vascular features) have been developed and added to new images.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In computerized methods being developed to assist medical professionals in various tasks [e.g., nodule or lung parenchyma segmentation, nodule classification, prediction of malignancy level, disease diagnosis like coronavirus (covid-19), etc.], augmentations of CT lung images are commonly provided by traditional techniques like rotation, flipping, and scaling (Table 2).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of lung CT images
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, it is not possible to determine augmentation methods’ contributions to those performances according to the presented results in those publications.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
plain- Asserted: fails — The authors may not put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. The quote only partly states this claim.
In the paper
“Similarly, noise and artifacts have been eliminated before the segmentation of the images into non-overlapping parts and augmentation (Karthiga et al. 2022).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
What the sources say
- Not checked yetcites Karthiga et al. 2022
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While doing a GAN based augmentation of lung CT scans, researchers usually tend to generate the area showing lung nodules in the whole image. Because the lung nodules are small and therefore it is more cost-effective than generating a larger whole image or background area.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of lung CT images
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Although it can be applied with vertical or/and horizontal axes, vertical flipping is rarely preferred since the bottom and top regions of an image may not always be interchangeable (Nalepa et al. 2019).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Commonly applied augmentation methods
What the sources say
- Not checked yetcites Nalepa et al. 2019
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, to obtain unbiased classification results, an equal number of images have been taken from the databases for the classes.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Materials and methods > Classification
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In those applications, different network architectures, datasets, and numbers of images have been used, and also their performances have been evaluated with different metrics. High performances have been obtained from those applications according to the reported results.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of brain MR images
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In a different study (Kurup et al. 2020), combination of rotation, translation, mirroring, and zooming has been used to achieve automated detection of malaria retinopathy.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
What the sources say
- Not checked yetcites Kurup et al. 2020
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“In the literature, augmentation of brain MR images have been performed with various methods such as rotation, noise addition, shearing, and translation to increase number of images and improve performances of several tasks (Table 1).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of brain MR images
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Another combined augmentation including shifting and blurring has been used by Tufail et al. (2021) before binary classification (as healthy and nonhealthy) and multi-class (5 classes) classification of images to differentiate different stages of DR.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
What the sources say
- Not checked yetcites Tufail et al. (2021)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For instance, image refinement has been provided by noise reduction and resizing steps, and then segmentation and augmentation steps have been applied (Zeiser et al. 2020).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
What the sources say
- Not checked yetcites Zeiser et al. 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For comparisons of the effectiveness of the augmentation methods in deep learning-based classifications, the same network architecture and expanded data sets with the augmented images from each augmentation methods have been used.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The papers published between 2020 and 2022 and mainly collected by Springer, IEEE Xplore, and ELSEVIER, have been reviewed, and a systematic and comprehensive review and analyses of the augmentation methods have been carried out.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, their effectiveness depends on the characteristics (e.g., color, intensity, etc.) of the images, and whether all significant visual features according to medical conditions exist in the training sets.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, it is not possible to determine augmentation methods’ contributions to those performances according to the presented results in those publications.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of brain MR images
plain- Asserted: fails — The authors may not put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. The quote only partly states this claim.
In the paper
“In this augmentation, different NV kinds (trees, wheels, brooms) have been generated depending on the expected shape and location of NVs to synthesize new images.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
What the sources say
- Not checked yetcites Ara´ujo et al. 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In those applications, different network models, data sets and number of images have been used, and also their performances have been evaluated with different metrics. High performances have been obtained from those applications according to the reported results.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of lung CT images
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“their effectiveness depends on the characteristics (e.g., color, intensity, etc.) of the images, and whether all significant visual features according to medical conditions exist in the training sets.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Conclusion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Manual evaluations are subjective as well as time-consuming (Xu et al. 2018) since they are based on the domain knowledge of the physician.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
What the sources say
- Not checked yetcites Xu et al. 2018
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“the GAN based augmentation might lead to biased results because of matching the generated images to the distribution of the target domain”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
What the sources say
- Not checked yetcites Ju et al. (2021)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“GANs have vanishing gradient and mode collapsing problems.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, they require strong computer resources, a long processing time, and hamper the quality of the generated images in case of running on computers having low computational power with constrained resources.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Since the collapse of the mode restricts the capacity of GAN architecture to be varied, this interconnection is detrimental for augmentations of medical images (Zhang 2021).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
What the sources say
- Not checked yetcites Zhang 2021
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“A synthetic image’s quality is characterized by a low level of distortions, fuzziness, noise (Thung and Raveendran 2009), and its feature distribution matching with the class label (Zhou et al. 2020; Costa et al. 2017; Yu et al. 2019).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
What the sources say
- Not checked yetcites Thung and Raveendran 2009
- Not checked yetcites Zhou et al. 2020
- Not checked yetcites Costa et al. 2017
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“obtaining satisfactory training results is not easy if the training procedure does not assure the symmetry and alignment of both generator and discriminator networks.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Artifacts can be added while synthesizing images, and therefore the quality of the produced images can be very low.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“GAN based augmentation methods can increase diversity.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“A significant problem is vanishing gradient and mode collapsing (Yi et al. 2019).”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
What the sources say
- Not checked yetcites Yi et al. 2019
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, the trained model can be unpredictable since GANs are complex structures and managing the coordination of the generator and discriminator is difficult.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Discussions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“GANs are complex structures and managing the coordination of the generator and discriminator is difficult.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper relies on this without support.
Medical image datasets are usually limited in size.
“However, the number of medical images is usually limited.”
The paper relies on this without support.
Scarcity and imbalance in medical image data cause overfitting, bias, and inaccurate results.
“Therefore, imbalanced data causing overfitting, biased and inaccurate results is a significant challenge that should be handled while developing deep network based methods.”
The paper relies on this without support.
Deep networks need training sets with many images covering all variations to achieve high generalization and robustness.
“Because training data sets should be constructed with a large number of images including all variations (i.e., heterogeneous images) to provide the high generalization ability and robustness of those architectures.”
The paper relies on this without support.
No earlier comprehensive review covers a wider range of medical imaging modalities and applications than this paper.
“According to our knowledge, there is no (i) comprehensive review that covers a greater range of medical imaging modalities and applications than articles in the literature”
The paper relies on this without support.
No earlier work quantitatively evaluates augmentation methods across different medical images to identify the most suitable method.
“and (ii) any work on quantitative evaluations of the augmentation methods applied with different medical images to determine the most appropriate augmentation method.”
The paper relies on this without support.
Eye fundus features such as neovascularization, hemorrhages/microaneurysms, and exudates are symptoms of eye diseases.
“Eye fundus images show various important features, which are symptoms of eye diseases, such as abnormal blood veins (neovascularization), red/reddish spots (hemorrhages, microaneurysms), and bright lesions (soft or hard exudates).”
The paper relies on this without support.
Image labels may not always be preserved if the degree of rotation increases.
“Image labels may not always be preserved if the degree of rotation increases.”
The paper relies on this without support.
The main challenging issues of GAN-based image augmentation are generating high-quality images and maintaining training stability.
“The main challenging issues of GAN based image augmentations are generating images with high quality (i.e., high-resolution, clear) and maintaining training stability.”
The paper relies on this without support.
Failures in detection or identification of breast lesions lead to unnecessary biopsies or inaccurate diagnoses.
“Because failures in the detection or identification of the lesions lead to unnecessary biopsies or inaccurate diagnoses.”
The paper relies on this without support.
Rotation transformation is generally safe on medical images.
“Although rotation transformation is not safe on images showing 6 and 9 in digit recognition applications, it is generally safe on medical images.”
The paper relies on this without support.
Quality and diversity are critical criteria for evaluating synthetic images from GANs.
“Quality and diversity are two critical criteria in the evaluation of synthetically generated images from GANs.”
The paper relies on this without support.
The validity of similarity measurements for medical image datasets is still under investigation and there is no accepted consensus.
“The validity of these measurements for medical image data sets is still under investigation and there is no accepted consensus.”
The paper relies on this without support.
There is no commonly accepted metric for evaluating the diversity and quality of synthesized images.
“Also, there is no commonly accepted metric for evaluating the diversity and quality of synthesized images.”
The paper relies on this without support.
The images used in the literature have been acquired from different types of fundus cameras.
“the images used in the literature have been acquired from different types of fundus cameras”
The paper relies on this without support.
Fundus images can be affected by pathological alterations.
“Also, images can be affected by pathological alterations.”
The paper relies on this without support.
The contributions of applied augmentation techniques to reported performances in the current literature are not clear.
“Also, the contributions of the applied augmentation techniques to the presented performances are not clear.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Laboratory performance does not always translate directly to clinical practice [142,143,144,145,146].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [143]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Medical image data augmentation: techniques, comparisons and interpretations > Results
“| 7th augmentation method | 85.24 |”
§ Medical image data augmentation: techniques, comparisons and interpretations > Results
“| 86.10 | 96.64 | 84.35 | 81.41 |”
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Medical image data augmentation: techniques, comparisons and interpretations > Results
“| 2nd augmentation method | 60.95 |”
§ Medical image data augmentation: techniques, comparisons and interpretations > Results
“| 63.13 | 73.57 | 60.28 | 51.97 |”
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Medical image data augmentation: techniques, comparisons and interpretations > Results
“| 10th augmentation method | 88.24 |”
§ Medical image data augmentation: techniques, comparisons and interpretations > Results
“| 90.48 | 97.13 | 88.52 | 82.79 |”
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Medical image data augmentation: techniques, comparisons and interpretations > Results
“| 6th augmentation method | 62.38 |”
§ Medical image data augmentation: techniques, comparisons and interpretations > Results
“| 65.16 | 70.06 | 62.51 | 56.08 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of lung CT images
“Accuracy, specificity, sensitivity, F1-score are 99.49%,”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of lung CT images
“99.25%, 99.25%, and 99.12%, respectively”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of lung CT images
“All methods applied for augmentation of breast mammography images in the publications reviewed in this study and the details in those publications are presented in Table 2.”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of lung CT images
“Table: Table 2 Augmentation methods applied with lung CT images and details given in publications”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
“Sensitivity, specificity, accuracy values are 90%, 100%, and 81% respectively”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of eye fundus images
“Malarial retinopathy detection”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
“accuracy, and sensitivity are 88.0%,86.1%, 87.0%, and 85.6%, respectively”
§ Medical image data augmentation: techniques, comparisons and interpretations > Related works > Augmentation of breast mammography images
“Image classification (as normal, and cancer)”
Citations
Cites
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A survey on Image Data Augmentation for Deep Learning
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Cited by
Sorted by citations
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SULBA: A Task-Agnostic Data Augmentation Framework for Deep Learning in Medical Image Analysis
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Hybrid Diffusion Model for Breast Ultrasound Image Augmentation
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An attention-based hybrid deep learning technique for brain tumor classification
Han, Q., & Ren, C. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05455-7
Reyes, H. E. Z., Barreto, H. P., & López-Armas, G. D. C. (2026) · Mathematics · doi:10.3390/math14122080 · Cited by 1
Data Augmentation for Image Classification: A Survey
Bhattacharya, S., Ray, P., Ghosh, S., Bhattacharjee, A., Dalui, I., Mondal, S., & Paul, A. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-21164-4_5
A VIN-Specific Empirical Study of Data Augmentation Effects on Sequence-Level OCR Reliability
Khalleefah, Z., Nasir, I., & Alkhadafe, H. (2026) · International Multi-Conference on Systems, Signals & Devices · doi:10.1109/ssd69655.2026.11559012
Deep Learning in Dental Imaging: Advances, Challenges, and Future
AYDIN, E. C., & Can, Z. (2026) · Oral Radiology · doi:10.1007/s11282-026-00939-1 · Cited by 1
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TinyEyeNet: An Efficient CNN for Classifying Anterior Segment Eye Conditions
Singh, A., Sharma, P. M., Kaushal, A., et al. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-27117-4_28
Guimarães, D. M., Szczupak, D., Campos, V. P., Bramati, I. E., Silva, A. C., & Tovar-Moll, F. F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.26.734817
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Deep Learning Based Pox Disease Detection and Generation of Synthesis Data with GAN Model
Şengöz, N., Vargün, E., & Köroğlu, H. (2026) · Turkish Journal of Mathematics and Computer Science · doi:10.47000/tjmcs.1725690
Automated Diabetes Stage Classification Using Tongue Visual Features
Kanakamedala, P., Rakesh, M., Ram, M. S., et al. (2026) · doi:10.1109/ci2a69097.2026.11577093
Saifullah, S., Dreżewski, R., Yudhana, A., Caesarendra, W., & Sela, E. I. (2026) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2026-0025 · Cited by 1
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Lesion-Aware Binary Diabetic Retinopathy Detection in an Imbalanced Fundus Image Dataset
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신수종, Chung, J., Cho, J., & Choi, S. (2026) · Diagnostics · doi:10.3390/diagnostics16142154
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Detection Of Pneumonia Based On Chest X-Ray Images Using Artificial Intelligence
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Lumber Spine Degeneration Classification Using MRI Images with Deep Learning Techniques
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Evolve While You Train: Online Data Augmentation for Medical Imaging
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Analyzing the Impact of Photometric Data Augmentation on Medical Instance Segmentation Performance
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A Hybrid Multi-Stage Framework for Pulmonary Disorder Classification Using Machine Learning
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Feature Copy-Paste with Fine-Grained Data Augmentation for Robust Lung Cancer Analysis in CT Images
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Artery and Vein Segmentation on Brain CTA Using CTP for Contrast-Phase Augmentation
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Exploring mesh-based features for enhanced dermoscopic lesion classification
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Metadata
- Authors
- Goceri, E.
- Venue
- Artificial Intelligence Review
- Published
- 2023-03-20
- Volume
- 56
- Issue
- 11
- Pages
- 12561-12605
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- PMC
- PMC10027281
- OpenAlex
- W4327909703
- Semantic Scholar
- da92fa7949632062a3eb57bdfe6244227cba28be
- Cited by
- 459
- References
- 116
- Citations by year
- 2026: 116 · 2025: 200 · 2024: 115 · 2023: 28
- FWCI
- 18.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Artificial Intelligence Review (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Keywords
- medical image classification, data scarcity, overfitting, data bias, neuroimaging, lung imaging, breast imaging, ocular imaging, magnetic resonance imaging, CT, mammography, fundus examination, imaging modalities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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