The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index; none of the 23 sources its claims cite is checked for support yet.
Self-supervised learning for medical image analysis using image context restoration
Open accessAbstract
Machine learning, particularly deep learning has boosted medical image analysis over the past years. Training a good model based on deep learning requires large amount of labelled data. However, it is often difficult to obtain a sufficient number of labelled images for training. In many scenarios the dataset in question consists of more unlabelled images than labelled ones. Therefore, boosting the performance of machine learning models by using unlabelled as well as labelled data is an important but challenging problem. Self-supervised learning presents one possible solution to this problem. However, existing self-supervised learning strategies applicable to medical images cannot result in significant performance improvement. Therefore, they often lead to only marginal improvements. In this paper, we propose a novel self-supervised learning strategy based on context restoration in order to better exploit unlabelled images. The context restoration strategy has three major features: 1) it learns semantic image features; 2) these image features are useful for different types of subsequent image analysis tasks; and 3) its implementation is simple. We validate the context restoration strategy in three common problems in medical imaging: classification, localization, and segmentation. For classification, we apply and test it to scan plane detection in fetal 2D ultrasound images; to localise abdominal organs in CT images; and to segment brain tumours in multi-modal MR images. In all three cases, self-supervised learning based on context restoration learns useful semantic features and lead to improved machine learning models for the above tasks.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper proposes context restoration as a self-supervised pretraining task for medical images: randomly swap image patches and train a CNN to restore the original context. Across fetal ultrasound classification, abdominal CT organ localization, and brain MR tumor segmentation, context restoration pretraining outperformed autoencoder, relative-position, context-prediction, and no self-supervision.
Key findings
- Context restoration pretraining improved CNNs in all three evaluated tasks: fetal standard scan plane classification, abdominal multi-organ localization, and brain tumor segmentation. It outperformed other self-supervised strategies and no self-supervision in the reported comparisons.
- For fetal ultrasound classification, when using only a quarter of the training data, context restoration pretraining improved the SonoNet performance the most among the compared pretraining strategies.
- For abdominal CT localization, context restoration pretraining produced more localization improvements than other pretraining methods; in some cases it was comparable to or better than no pretraining on more annotated training data.
- For BraTS 2017 brain tumor segmentation, U-Nets initialized by context restoration pretraining achieved the best performance in total; using 50% of the training set gave similar performance to using the whole training set, with Dice in enhanced tumor core, sensitivity in non-enhanced and enhanced tumor cores, and Hausdorff distances even slightly better.
Methods
- Self-supervised task: given an unlabelled image, randomly select two isolated patches and swap them; repeat T times to disorder context, then train a CNN to restore the original image using an L2 loss.
- Evaluated on three datasets/tasks: fetal 2D ultrasound classification with 2694 examinations and SonoNet-64; abdominal CT multi-organ localization with 150 subjects and a SonoNet-like CNN; BraTS 2017 brain tumor segmentation with 285 subjects, 142 for training/validation and 143 for testing, using a 2D U-Net on 64×64 patches.
- Compared pretraining strategies: autoencoder, relative position prediction, local context prediction, and context restoration; subsequent tasks used whole, half, or quarter of the training set. Training details include 58K iterations for ultrasound classification, 55K for CT localization, and 50K for segmentation.
Limitations
- The paper states that the L2 loss used for context restoration results in blurry restored images.
- The paper reports that data augmentation did not consistently improve performance and sometimes decreased segmentation accuracy; it also notes that self-supervised pretraining was not compared against supervised pretraining because supervised pretraining is difficult in medical imaging due to annotation requirements.
- Evident from the text: evaluation is limited to three medical imaging tasks and datasets, and the paper notes that in brain tumor segmentation the performance differences among self-supervision strategies were not always significant.
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Claims the paper makes
26 Own interpretation · 25 Own result · 21 No basis given · 20 Not checked yet
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
“Balancing the numbers of instances in each class significantly improves the performance in all three metrics.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Fetal standard scan plane classification
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.
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
“Initialising by pretrained features, particularly those from context restoration tasks, improves the CNN performance.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Abdominal multi-organ localization
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.
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
“A shortcoming is that the L2 loss results in image blur.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Context restoration results
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.
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 all three cases, self-supervised learning based on context restoration learns useful semantic features and lead to improved machine learning models for the above tasks.”
§ Self-supervised learning for medical image analysis using image context restoration > Abstract
plaindirectn = 3- 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.
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
“On quarter of training data, the data augmentation techniques can be advantageous over some self-supervised learning methods such as the CP method.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Fetal standard scan plane classification
plainconditional- 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.
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
“when learning on half of training images, the F1-scores keep stable in most cases except where the SonoNet is pretrained based on context restoration.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Fetal standard scan plane classification
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.
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
“Our context restoration pretraining improves the SonoNet performance the most.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Fetal standard scan plane 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.
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 all cases, the image context restoration achieve qualitatively good results.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Context restoration results
plain- Asserted: passes — The authors put it forward as true.
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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 did not offer performance improvement in many cases.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Abdominal multi-organ localization
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.
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
“using 50% of the training set, the proposed self-supervision strategy offers similar performance to using the whole training set.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Brain tumour segmentation
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.
- It may state two separate claims as one. 18
- The quote only partly states this claim. 1
- Probably not a substantial claim. 21
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
“when the SonoNet is trained on quarter of the training data, the precision decreases significantly while there is only slight decrease in terms of the recall.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Fetal standard scan plane classification
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 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 all three tasks, the pretraining based on our context restoration strategy is superior to other self-supervised learning strategies, as well as no self-supervised training.”
§ Self-supervised learning for medical image analysis using image context restoration > Introduction
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.
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
“When the SonoNet is trained on half of the training data, the precision and recall both decrease, which lead to the decrease of the F1-score.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Fetal standard scan plane classification
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 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
“Data augmentation does not lead to improvement when the SonoNet is trained on half data; while it lead to obvious improvement when the SonoNet is trained on quarter of data.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Fetal standard scan plane classification
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
“The SonoNet pretrained using context restoration can only offers marginal improvement.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Fetal standard scan plane classification
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.
The quote only partly states this claim.
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
“When learning using only a quarter of training images, the SonoNet with feature initialisation from the auto-encoder pretraining still cannot improve the baseline; while SonoNets using other pretraining strategies perform better than the baseline.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Fetal standard scan plane classification
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.
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 terms of the liver, the CNN performance decreases sharply with half training data; while it remains stable with quarter training data.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Abdominal multi-organ localization
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
“Although the CNN pretrained using the RP method on quarter training data outperforms that using context restoration pretraining, it performs much worse in left kidney.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Abdominal multi-organ localization
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
“The Dice score in enhanced tumour core, the sensitivity in non-enhanced and enhanced tumour cores, and the Hausdorff distances in all aspects are even slightly better.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Brain tumour segmentation
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 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
“pretraining based on relative positions and context prediction tasks improves the segmentations but they are not as good as the pretraining based on the context restoration task.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Brain tumour segmentation
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
“For many common problems in medical imaging such as classification, localization, and segmentation, learning image context is key.”
§ Self-supervised learning for medical image analysis using image context restoration > Self-supervision based on context restoration > Context restoration
plain- 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.
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
“In case of subsequent segmentation tasks, the whole CNN architecture can be shared by the pretraining network and the subsequent segmentation network. As a result, almost all the weights of the subsequent segmentation CNN can be initialised using those learned in the self-supervised pretraining. This results in better segmentation results.”
§ Self-supervised learning for medical image analysis using image context restoration > Self-supervision based on context restoration > Network architectures
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“However, the removal of context changes the image intensity distribution. Thus the resulting images belong to another domain and the learned features may not be useful for images in the original domain.”
§ Self-supervised learning for medical image analysis using image context restoration > Related work
What the sources say
- Not checked yetcites Pathak et al., 2016
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — 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
“The pretrained models from the natural image domain are not useful in the medical imaging domain since the intensity distribution of natural images is different from that of medical images.”
§ Self-supervised learning for medical image analysis using image context restoration > 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.
In the paper
“Since each data instance is regarded as one class, the exemplar learning method is difficult to apply to large datasets.”
§ Self-supervised learning for medical image analysis using image context restoration > Related work
What the sources say
- Not checked yetcites Dosovitskiy et al., 2016 source
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
“More precisely, the CNN weights learned in the CP method can be used to initialise subsequent CNNs for classification and segmentation; while CNN weights learned in the RP method can initialise subsequent classification CNNs and only the analysis part of the subsequent segmentation CNNs.”
§ Self-supervised learning for medical image analysis using image context restoration > Related work
What the sources say
- Not checked yetcites Pathak et al., 2016
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. It may state two separate claims as one.
In the paper
“In case of subsequent classification tasks, simple CNN layers with a few deconvolution layers are sufficient (see Fig. 3) (Doersch and Zisserman, 2017). In this condition, the analysis part makes most contributions to the context restoration. Therefore, the feature maps resulting from the analysis part are more useful.”
§ Self-supervised learning for medical image analysis using image context restoration > Self-supervision based on context restoration > Network architectures
What the sources say
- Not checked yetcites Doersch and Zisserman, 2017
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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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
“This may because the augmented images do not cover the variance of organ shapes and positions and introduce additional variance to pixel intensities.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Abdominal multi-organ localization
hedgedconditional- 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
“The disadvantage of multi-task self-supervised learning is the training requires significant computational resources, i.e. 64 GPUs for approximately 16.8K GPU hours.”
§ Self-supervised learning for medical image analysis using image context restoration > Related work
What the sources say
- Not checked yetcites Doersch and Zisserman, 2017
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
“In many medical image analysis problems, localization anatomical structures is a prerequisite.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Abdominal multi-organ localization
What the sources say
- Not checked yetcites Heimann et al., 2009 source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“As suggested by Pathak et al. (2016), the L2 loss is sufficient for feature learning although the outputs from context restoration outputs may be blurry.”
§ Self-supervised learning for medical image analysis using image context restoration > Self-supervision based on context restoration > Network architectures
What the sources say
- Not checked yetcites Pathak et al., 2016
hedgeddirect- 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.
- 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
“In this scenario, the baseline (i.e. random initialisation) is not far away from the ceiling (i.e. SonoNet on the whole training set). Therefore, it is difficult to obtain improvements.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Fetal standard scan plane classification
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“de Vos et al. (2017) proposed a novel approach which can localize anatomical structures in 3D medical images.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Abdominal multi-organ localization
What the sources say
- Not checked yetcites de Vos et al., 2017 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“The results showed that multitask self-supervised learning improves subsequent tasks more than single-task self-supervised learning.”
§ Self-supervised learning for medical image analysis using image context restoration > Related work
What the sources say
- Not checked yetcites Doersch and Zisserman, 2017
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.
None of the sources cited for this claim have been checked yet.
In the paper
“However, existing self-supervised learning strategies applicable to medical images cannot result in significant performance improvement. Therefore, they often lead to only marginal improvements.”
§ Self-supervised learning for medical image analysis using image context restoration > Abstract
What the sources say
- Not checked yetcites Doersch et al., 2015
- Not checked yetcites Pathak et al., 2016
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“the CNNs for context restoration can be structured in flexible architectures depending on subsequent tasks.”
§ Self-supervised learning for medical image analysis using image context restoration > Discussion and conclusion
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 may give no reason why it holds.
In the paper
“They are routinely assessed using MR imaging (Bakas et al., 2017b).”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Brain tumour segmentation
What the sources say
- Not checked yetcites Bakas et al., 2017b
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.
None of the sources cited for this claim have been checked yet.
In the paper
“In computer vision, many CNNs are pretrained before the main task.”
§ Self-supervised learning for medical image analysis using image context restoration > Discussion and conclusion
What the sources say
- Not checked yetcites Ren et al., 2015
- Not checked yetcites Simonyan and Zis-serman, 2014
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.
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
“the implementation of the context restoration task is simple and straightforward, meaning that it can be widely used.”
§ Self-supervised learning for medical image analysis using image context restoration > Discussion and conclusion
plaindirect- 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. 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
“2) The augmentation does not introduce any more useful variance in terms of tumour appearance.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Brain tumour segmentation
plainconditional- 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 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
“3) The augmentation introduces additional intensity variations which are not meaningful.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Brain tumour segmentation
plainconditional- 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
“the Faster R-CNN (Ren et al., 2015) is based on the pretraining of the VGG-Net (Simonyan and Zis-serman, 2014).”
§ Self-supervised learning for medical image analysis using image context restoration > Discussion and conclusion
What the sources say
- Not checked yetcites Ren et al., 2015
- Not checked yetcites Simonyan and Zis-serman, 2014
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
“which may because of three reasons: 1) The segmentation is based on patches so a large number of image patches can be sampled.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Brain tumour segmentation
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
“We conclude from the existing self-supervised feature learning literature that the ideal pretraining task should have similar goal to the subsequent task.”
§ Self-supervised learning for medical image analysis using image context restoration > Discussion and conclusion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check 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. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“This type of pretraining leads to good detection results in the Faster R-CNN.”
§ Self-supervised learning for medical image analysis using image context restoration > Discussion and conclusion
What the sources say
- Not checked yetcites Ren et al., 2015
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
“It is noteworthy that if less training data leads to significant decrease of results, self-supervised learning is likely to improve the results significantly.”
§ Self-supervised learning for medical image analysis using image context restoration > Experiments and results > Abdominal multi-organ localization
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: 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.
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
“if the reduction of training data causes significant performance decrease, the context restoration pretraining can offer significant performance improvement over the baselines.”
§ Self-supervised learning for medical image analysis using image context restoration > Discussion and conclusion
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
“Compared with the existing strategies such as relative positions and context prediction, solving the context restoration task requires pattern recognition and prediction, which ensures the context restoration task offers more efficient image semantics.”
§ Self-supervised learning for medical image analysis using image context restoration > Discussion and conclusion
plaindirect- 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.
The paper relies on this without support.
Training CNNs only using a small number of labelled images cannot always achieve satisfactory results and does not exploit the potentially large number of unlabelled images that may be available.
“Training CNNs only using the small number of labelled images cannot always achieve satisfactory results and does not exploit the potentially large number of unlabelled images that may be available.”
The paper relies on this without support.
It is usually easier to access a large number of unlabelled medical images rather than a large number of annotated images.
“This means it is usually easier to access a large number of unlabelled medical images rather than a large number of annotated images.”
The paper relies on this without support.
In medical imaging, most images are in greyscale so that no colour information is available.
“In medical imaging, most images are in greyscale so that no colour information is available.”
The paper relies on this without support.
The key challenge for self-supervised learning is identifying a suitable self-supervision task, i.e. generating input and output instance pairs from data.
“The key challenge for self-supervised learning is identifying a suitable self supervision task, i.e. generating input and out-put instance pairs from data.”
The paper relies on this without support.
Manual cropping requires expert knowledge and is costly.
“However, manual cropping requires expert knowledge and costly.”
The paper relies on this without support.
Not all patients have two kidneys.
“This is because not all patients have two kidneys.”
The paper relies on this without support.
Gliomas are the major brain tumours occurring in adults.
“Gliomas are the major brain tumours occurring in adults.”
The paper relies on this without support.
Accurate segmentation of gliomas on MR image is a key step for quantification.
“Accurate segmentation of gliomas on MR image is a key step for quantification.”
The paper relies on this without support.
In medical image analysis, the image context is the common feature for classification, localization/detection, and segmentation tasks.
“Particularly, in medical image analysis, the image context is the common feature for classification, localization/detection, and segmentation tasks.”
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 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
§ Self-supervised learning for medical image analysis using image context restoration > Self-supervision based on context restoration > Context restoration
“a new dataset (1)χ˜=f(χ˜) is generated.”
§ Self-supervised learning for medical image analysis using image context restoration > Self-supervision based on context restoration > Context restoration
“xi=g(x˜i)=f−1(x˜i)”
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Densely Connected Convolutional Networks
Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2016) · arXiv · doi:10.48550/arxiv.1608.06993 · Cited by 48,960
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Cited by
Sorted by citations
Items 401–500 of 573
iSSL-AL: a deep active learning framework based on self-supervised learning for image classification
Agha, R., Mustafa, A. M., & Abuein, Q. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10271-6 · Cited by 2
Cai, Z., Zhong, Z., Lin, H., Huang, B., Xu, Z., Huang, B., Deng, W. G., Wu, Q., Lei, K., Lyu, J., et al. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102471 · Cited by 2
Vatanparast, A., Fateh, M., & Mashayekhi, H. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108586 · Cited by 2
Consensus-guided evaluation of self-supervised learning in echocardiographic segmentation
Naidoo, P., Fernandes, P., Serej, N. D., Manisty, C., Shun-Shin, M. J., Francis, D. P., & Zolgharni, M. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111148 · Cited by 2
Generalization of artificial intelligence models for embryo selection
Liu, H., Shan, G., Zhang, Z., & Sun, Y. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26745-1.00012-8 · Cited by 2
SupReMix: Supervised contrastive learning for medical imaging regression with mixup
Wu, Y., Dong, Z., Chen, C., Zhou, W., & Zhou, J. H. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103909 · Cited by 5
Automated Coregistered Segmentation for Volumetric Analysis of Multiparametric Renal MRI
Ghoul, A., Liang, C., Loster, I., Umapathy, L., Kuehn, B., Martirosian, P., Seith, F., Gatidis, S., & Küstner, T. (2026) · Magnetic Resonance in Medicine · doi:10.1002/mrm.70288 · Cited by 2
Nakashima, M., Qiu, J., Huang, P., Lee, J., Chen, P. M., Grimm, R. A., Nguyen, C. T., Kim, B.-H., Zhao, D., Kwon, D. H., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-73022-2 · Cited by 2
Kaan, A. R. I. K. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10702-8 · Cited by 3
Predicting effectiveness of anti-VEGF injection through self-supervised learning in OCT images
Feng, D., Chen, X., Wang, X., Mou, X., Bai, L., Zhang, S., & Zhou, Z. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023114 · Cited by 2
CLCA: Contrastive Learning Using Combined Additional Information for Alzheimer's Diagnosis
Chen, Y., Lou, S., Shuai, M., He, K., & An, Z. (2023) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice58320.2023.10105726 · Cited by 2
Talasila, A., Karthikeyan, A., Alle, S., Maity, M., & Priyakumar, U. D. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191913 · Cited by 2
Hybrid-supervised bidirectional transfer networks for computer-aided diagnosis
Gong, R., Shi, J., Wang, J., Wang, J., Zhou, J., Lu, X., Du, J., & Shi, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107409 · Cited by 2
An Evaluation of Self-Supervised Learning for Portfolio Diversification
Yang, Y., & Hospedales, T. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-44213-1_24 · Cited by 2
Using a deep generation network reveals neuroanatomical specificity in hemispheres
Wang, G., Jiang, N., Ma, Y., Suo, D., Liu, T., Funahashi, S., & Yan, T. (2024) · Patterns · doi:10.1016/j.patter.2024.100930 · Cited by 1
Rajesh Sharma, R., Prakash, K., Sungheetha, A., Venugopal, E., Karthik, S. V. S., & Ghantasala, G. S. P. (2024) · International Conference on Sustainable Expert Systems (ICSES) · doi:10.1109/icses63445.2024.10763290 · Cited by 2
Tisha, S. N., & Hamerly, G. (2024) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz64857.2024.10794468 · Cited by 2
CrackSAM: Study on Few-Shot Segmentation of Mural Crack Images Using SAM
Han, X., Li, C., Jiang, X., Zhang, S., Wang, S., & Zhang, H. (2024) · IEEE International Conference on Advanced Infocomm Technology · doi:10.1109/icait62580.2024.10808070 · Cited by 2
AI-Powered Formulation Design: Neural Networks in Drug Development
Pashikanti, S., N., S., Sanapala, R., & Shiva Kumar., M. (2025) · Research Journal of Pharmaceutical Dosage Forms and Technology · doi:10.52711/0975-4377.2025.00005 · Cited by 2
Prabu, S., Chandra, N. S., Bhavani, M., Varma, V. K. C., & Soni, M. (2025) · International Conference on Integrated Circuits and Communication Systems (ICICACS) · doi:10.1109/icicacs65178.2025.10968948 · Cited by 2
RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images
Li, G., Togo, R., Ogawa, T., & Haseyama, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91578-9_9 · Cited by 3
Chen, Q., Yao, X., Ye, H., & Hong, Y. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3609739 · Cited by 3
Zhu, J., Wang, S., He, J., Schönlieb, C., & Yu, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17266-3_11 · Cited by 2
Self Supervised Learning for Classifying the Rotated Images
Roy, S., Marathe, A., Walambe, R. A., & Kotecha, K. V. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-35644-5_2 · Cited by 2
Reduction of Annotation Effort in Medical Image Analysis Based on Self-supervised Learning
Chan, K.-H., & Zeng, Y. (2023) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc58517.2023.10317280 · Cited by 2
Zhou, H.-Y., Wang, C., Li, H., Wang, G., Zhang, S., Li, W., & Yu, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.01544 · Cited by 2
A self-supervised deep learning pipeline for segmentation in two-photon fluorescence microscopyPreprint
Ntiri, E. E., Xu, T., Rozak, M. W., Attarpour, A., Dorr, A. E., Stefanovic, B., & Goubran, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.20.633744 · Cited by 2
Zhu, B., Yang, F., Duan, H., & Gao, Z. (2024) · Current Bioinformatics · doi:10.2174/0115748936295679240620094626 · Cited by 1
Fetal Ultrasound Video Representation Learning Using Contrastive Rubik’s Cube Recovery
Zhang, K., Jiao, J., & Noble, J. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73647-6_18 · Cited by 1
Artificial Intelligence in Healthcare: India Case Study
Mathew, R. S., Paluru, N., & Yalavarthy, P. K. (2024) · Springer eBooks · doi:10.1007/978-981-97-0896-3_8 · Cited by 1
Correlation Weighted Prototype-Based Self-supervised One-Shot Segmentation of Medical Images
Manna, S., Bhattacharya, S., & Pal, U. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78192-6_2 · Cited by 1
Brain Tumor Segmentation Based on Self-supervised Pre-training and Adaptive Region-Specific Loss
Zhou, Y., Zhong, L., & Wang, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-76163-8_5 · Cited by 1
Shah, S. M. A. H., Rizwan, A., Sardaraz, M., Tahir, M., Samee, N. M. A., & Jamjoom, M. M. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122239 · Cited by 1
Future Trends in Artificial Intelligence for Medical Applications
Chaudhuri, T., & Ghosh, J. (2025) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/979-8-3693-9816-6.ch020 · Cited by 1
Zhou, Y., Wu, J., Fu, J., Yue, Q., Liao, W., Zhang, S., Zhang, S., & Wang, G. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3592116 · Cited by 1
Applications of Computer Vision and Machine Learning in Bioimaging
Argunşah, A. Ö., Erdil, E., & Ünay, D. (2025) · Springer eBooks · doi:10.1007/978-3-031-99410-4_28 · Cited by 2
Manchester, T., Anders, A., Spadotto, J. C., Eccleston, H., Beavan, W., Arcis, H., & Connolly, B. J. (2025) · Journal of Microscopy · doi:10.1111/jmi.70032 · Cited by 2
Qi, X., Zhang, G., Wu, J., Zhang, S., Hou, X., & Wang, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04984-1_19 · Cited by 1
Volume Fusion-Based Self-Supervised Pretraining for 3D Medical Image Segmentation
Wang, G., Fu, J., Wu, J., Luo, X., Zhou, Y., Liu, X., Li, K., Lin, J., Shen, B., & Zhang, S. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3610249 · Cited by 1
Multiclass classification in medical imaging using self-supervised learning vs supervised learning
Kumari, N., & Agarwal, S. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-08085-x · Cited by 1
AnaCoMT+: Anatomy-Aware Symmetric Contrast and Cross-Modal Transfer for Brain Tumor Segmentation
Fu, Y. F., Liu, C., Wang, S., Liu, Z., Wang, T., Xia, H., Su, C., Song, R., & Zhao, Q. (2026) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3786796 · Cited by 1
Xie, M., Xiao, Q., Wu, H., Zhang, Y., Han, H., Xie, X., Zhang, W., Cheng, J., & Xu, J. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02347-5 · Cited by 1
FP-MAE: A Self-Supervised Model for Floorplan Generation with Incomplete Inputs
Zhong, J., Luo, R., Li, P., Li, T., Zeng, P., Lei, Z., Feng, T., & Yin, J. (2026) · Buildings · doi:10.3390/buildings16030558 · Cited by 1
Active test-time adaptation for continual medical image classification
Zhao, K., Song, G., ge, C., Hu, W., & Liu, X. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113288 · Cited by 1
Yang, J., Yuan, J., & Li, W. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114765 · Cited by 1
Wei, W., & Yuan, Y. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3687158 · Cited by 1
Multi-level information fusion for explainable diagnosis of melanoma using dermoscopic images
Sun, R., & Rostami, M. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102761 · Cited by 1
Islam, B., Maruf, S., Rahman, T., Rezwona, H., Gani, R., Rashid, M. R. A., Islam, R. U., Reza, A. W., & Ripon, S. (2025) · IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON) · doi:10.1109/becithcon69222.2025.11504014 · Cited by 1
Automated detection of cerebral microbleeds on ex-vivo MRI scans of community-based older adults
Nikseresht, G., Evia, A. M., Gady Agam, B., Bennett, D. A., Schneider, J. A., & Arfanakis, K. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121986 · Cited by 1
Han, X., Wu, Z. W., Wang, Z., Dong, S., Zhang, R., Liu, J., & Zhang, L. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114411 · Cited by 2
Learning from pseudo-lesion: a self-supervised framework for COVID-19 diagnosis
Li, Z., Li, X., Jin, Z., & Shen, L. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08259-9 · Cited by 1
Medical Image Classification for Disease Prediction with the AID of Machine Learning Approach
Illa, P. K., Kumar, T. S., Bhardwaj, A., Prakash, S., Adakane, P. K., & Verma, D. (2022) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite53722.2022.9823839 · Cited by 1
Quesada, J. R., Sathidevi, L., Liu, R., Ahad, N., Jackson, J. M., Azabou, M., Xiao, J., Liding, C., Jin, M., Urzay, C., et al. (2023) · PubMed · doi:10.48550/arxiv.2301.00345 · Cited by 1
Wang, Y., Liu, B., & Zhou, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25825-1_36 · Cited by 1
Kalaiselvi, K., & Kasthuri, M. (2023) · Indian Journal of Science and Technology · doi:10.17485/ijst/v16i13.138 · Cited by 1
Wang, X., Wang, R., Tian, B., Zhang, J., Zhang, S., Chen, J., Lukasiewicz, T., & Xu, Z. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094657 · Cited by 1
Self-supervised Learning for 3D CT Scan Segmentation
Shen, B., & Low, Y. K. T. (2023) · Springer eBooks · doi:10.1007/978-981-19-7222-5_21 · Cited by 1
Benjamin, J. G., Asokan, M., Alhosani, A., Alasmawi, H., Diehl, W., Bricker, L., Nandakumar, K., & Yaqub, M. K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44521-7_7 · Cited by 1
Machine Learning Based Diabetic Retinopathy Detection and Classification
Long, F., Sang, J., & Alam, M. S. (2023) · Springer eBooks · doi:10.1007/978-3-031-34873-0_5 · Cited by 1
Chai, S., Xin, J., Wu, J., Yu, H., Liang, Z., Ma, Y., & Zheng, N. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385369 · Cited by 1
A self-supervised fusion network for carotid plaque ultrasound image classification
Zhang, Y., Gan, H., Wang, F., Cheng, X., Wu, X., Yan, J., Yang, Z., & Zhou, R. (2024) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2024138 · Cited by 1
Evaluating the performance of hyperparameters for unbiased and fair machine learning
Bui, V., Yu, H., Kantipudi, K., Yaniv, Z. R., & Jaeger, S. (2024) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.3006909 · Cited by 1
Self-supervised few-shot medical image segmentation with spatial transformations
Titoriya, A. K., Singh, M. P., & Singh, A. K. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10184-4 · Cited by 1
Lu, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3496748 · Cited by 1
Chu, S., Ren, X., Ji, G., Zhao, J., Shi, J., Wei, Y., Pei, B., & Qiang, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3505303 · Cited by 1
Learning Anatomy-Disease Entangled Representation
Haghighi, F., Gotway, M. B., & Liang, J. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00406 · Cited by 1
A diabetic retinopathy classification method based on image-text contrastive learning
Long, F., Xiong, H., Hu, H., Qian, C., & Sang, J. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.04.017 · Cited by 1
Few-shot medical image segmentation via query transformation learning
Zheng, S., Wu, H., Gao, Z., & Li, P. (2025) · The Visual Computer · doi:10.1007/s00371-025-03945-2 · Cited by 1
Improving Fast MRI Reconstructions with Pretext Learning in Low-Data Regime
Jethi, A. K., de Souza, R. M., Ram, K., & Sivaprakasam, M. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871369 · Cited by 1
Torop, M., Kothapalli, S. V. V. N., Sun, Y., Liu, J., Kahali, S., Yablonskiy, D. A., & Kamilov, U. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.07087 · Cited by 1
Online Unsupervised Learning for Domain Shift in Covid-19 CT Scan DatasetsPreprint
Ewen, N., & Khan, N. (2023) · doi:10.32920/22734404 · Cited by 1
Targeted Self Supervision for Classification on a Small COVID-19 CT Scan DatasetPreprint
Ewen, N., & Khan, N. (2023) · doi:10.32920/22734284.v1 · Cited by 1
Ccbir: A Concept-Based System for Geospatial Image RetrievalPreprint
Vlachos, E., & Pitas, I. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5264255 · Cited by 1
Region-of-Interest Guided Supervoxel Inpainting for Self-supervision
Kayal, S., Chen, S., & de Bruijne, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59710-8_49
Lee, Y., Lee, S.-W., & Hwang, S. (2021) · Journal of Korean Institute of Industrial Engineers · doi:10.7232/jkiie.2021.47.2.180
Zhou, H.-Y., Wang, C., Li, H., Wang, G., Zhang, S., Li, W., & Yu, Y. (2021) · medRxiv · doi:10.1101/2021.06.02.21258256
A Positive/Unlabeled Approach for the Segmentation of Medical Sequences using Point-Wise Supervision
Lejeune, L., & Sznitman, R. (2021) · Figshare · doi:10.5281/zenodo.5007788
Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative $R_2^\ast$ MappingPreprint
Xu, X., Kothapalli, S. V. V. N., Liu, J., Kahali, S., Gan, W., Yablonskiy, D. A., & Kamilov, U. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.01622
Mahapatra, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.00404
Nezhad, A. P., Ghatee, M., & Sajedi, H. (2021) · doi:10.36227/techrxiv.14806290.v1
GraVIS: Grouping Augmented Views From Independent Sources for Dermatology Analysis
Zhou, H.-Y., Lu, C., Wang, L., & Yu, Y. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3216005
Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis
Bahrami, M., Ghorbani, M., Yeganeh, Y., & Navab, N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21014-3_40
Semi-supervised Multi-domain Learning for Medical Image Classification
Chavhan, R., Banerjee, B., & Das, N. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-23599-3_3
A ROI based self-supervised strategy for retinal image analysis
董世运, & Chen, Q. (2023) · International Conference on Optical and Photonic Engineering (icOPEN) · doi:10.1117/12.2667274
A COVID-19 detection method based on self-supervised learning and transfer learning
Lu, Z., Chen, Y., Chu, Z., Chen, S., & Sun, B. (2022) · International Conference on Wearables, Sports and Lifestyle Management (WSLM) · doi:10.1109/wslm54683.2022.00018
Challenges and future directions
Liao, H., Zhou, S. K., & Luo, J. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824383-1.00019-8
Deep Learning Approaches for Contrast Removal from Contrast-enhanced CT
Ganß, M., Benetti, F. D., Brosch-Lenz, J., Uribe, C., Shi, K., Eiber, M. J., Navab, N., & Wendler, T. (2023) · Informatik aktuell · doi:10.1007/978-3-658-41657-7_18
Online Unsupervised Learning for Domain Shift in Covid-19 CT Scan DatasetsPreprint
Ewen, N., & Khan, N. (2023) · doi:10.32920/22734404.v1
An Automatic Solution in Security Inspection
Zhang, H., & Zhang, X. (2023) · International Conference on Machine Vision and Information Technology (CMVIT) · doi:10.1109/cmvit57620.2023.00032
Self-supervised approach for organs at risk segmentation of abdominal CT images
Francis, S., Minino, C. A., Pournami, P. N., Puzhakkal, N., & Jayaraj, P. B. (2023) · ITM Web of Conferences · doi:10.1051/itmconf/20235401003
Kumar, R., & Arulkumar, N. (2023) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/978-1-6684-7100-5.ch013
Mask detection system at the entry of a room
Herdiyanto, E., & Fadlil, A. (2023) · Signal and Image Processing Letters · doi:10.31763/simple.v5i1.49
Predicting Disease(s) using Machine Learning in Medical Science
Benmechiche, M., NAIDJA, A., & BENNACER, L. (2023) · International journal of research studies in computer science and engineering · doi:10.20431/2349-4859.0901004
Zhai, P., Zhu, E., Wei, X. S., & Li, J. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230432
Self- Supervised Learning Based Covid-19 Detection from Chest Radiography Images Using Deep CNN
S, A. S. A., & Palanikkumar, D. (2023) · International Conference on IoT, Communication and Automation Technology (ICICAT) · doi:10.1109/icicat57735.2023.10263698
Metadata
- Authors
- Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., & Rueckert, D.
- Affiliations
- Imperial College London (GB)
- Nagoya University (JP)
- Aichi Cancer Center (JP)
- Nagoya University Hospital (JP)
- Corresponding author
- Chen, Liang
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 58
- Pages
- 101539
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC7613987
- OpenAlex
- W2964744899
- Semantic Scholar
- d6e78069dbbb35592f013187ef3432286def8f18
- Cited by
- 575
- References
- 73
- Citations by year
- 2026: 44 · 2025: 85 · 2024: 99 · 2023: 133 · 2022: 105 · 2021: 74 · 2020: 34 · 2019: 1
- FWCI
- 24.53
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Funders
- Wellcome Trust (102431)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- self-supervised learning, unlabeled data, CT images, brain tumor segmentation, multimodal MRI, image classification, image localization, image segmentation
- MeSH terms
- Abdomen, Supervised Machine Learning, Brain Neoplasms, Humans, Magnetic Resonance Imaging, Tomography, X-Ray Computed, Ultrasonography, Prenatal
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