The DOI matches its Crossref record; 11 of the 13 references it cites were found in a registry or an index.
Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans
Abstract
Convolutional neural networks (CNNs) have emerged as the most powerful technique for a range of different tasks in computer vision. Recent work suggested that CNN features are generic and can be used for classification tasks outside the exact domain for which the networks were trained. In this work we use the features from one such network, OverFeat, trained for object detection in natural images, for nodule detection in computed tomography scans. We use 865 scans from the publicly available LIDC data set, read by four thoracic radiologists. Nodule candidates are generated by a state-of-the-art nodule detection system. We extract 2D sagittal, coronal and axial patches for each nodule candidate and extract 4096 features from the penultimate layer of OverFeat and classify these with linear support vector machines. We show for various configurations that the off-the-shelf CNN features perform surprisingly well, but not as good as the dedicated detection system. When both approaches are combined, significantly better results are obtained than either approach alone. We conclude that CNN features have great potential to be used for detection tasks in volumetric medical data.
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“It is likely that artificial intelligence (AI) technologies will be incorporated into clinical practise, with applications such as image-based diagnostics in radiology demonstrating accuracy and efficacy rivalling speciality trained physicians [1–2].”
From Exploration of exposure to artificial intelligence in undergraduate medical education: a Canadian cross-sectional mixed-methods study (2022) · cites this paper as [2]
In the citing paper
“It is likely that artificial intelligence (AI) technologies will be incorporated into clinical practise, with applications such as image-based diagnostics in radiology demonstrating accuracy and efficacy rivalling speciality trained physicians [1–2].”
From Exploration of exposure to artificial intelligence in undergraduate medical education: a Canadian cross-sectional mixed-methods study (2022) · cites this paper as [2]
In the citing paper
“To complement “off-the-shelf” CNN features, [10], [9], [12] all add and integrate various other hand-crafted image features as hybrid inputs for the final CADe classification.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [9]
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Lung Segmentation-Based Pulmonary Disease Classification Using Deep Neural Networks
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MethodLung Nodule Classification on Computed Tomography Images Using Fractalnet
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Muzammil, M. J., Ali, I., Haq, I. U., Amir, M., & Abdullah, S. (2021) · IEEE Access · doi:10.1109/access.2021.3102707 · Cited by 32
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OMS-CNN: Optimized Multi-Scale CNN for Lung Nodule Detection Based on Faster R-CNN
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MethodRbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion
Rashad, M., Afifi, I., & Abdelfatah, M. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00769-7 · Cited by 28
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“Ginneken et al. [11] used features of the OverFeat network that trains object detection in natural images to detect nodules in computed tomography scans.”
BackgroundAn improvement for the automatic classification method for ultrasound images used on CNN
Avazov, K., Abdusalomov, A. B., Mukhiddinov, M., Baratov, N., Makhmudov, F., & Cho, Y. I. (2021) · International Journal of Wavelets Multiresolution and Information Processing · doi:10.1142/s0219691321500545 · Cited by 23
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“In committee-fusion, the predictions output by each soft-max layer of the 3 architectures are combined into a single probability with weighed averaging or a specific product rule (Van Ginneken et al. 2015).”
A Computer-Aided Detection System for the Detection of Lung Nodules Based on 3D-ResNet
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Computer-aided grading of prostate cancer from MRI images using Convolutional Neural Networks
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“[13] extracted three axial plane features of the detected candidate pulmonary nodules.”
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Identifying Carotid Plaque Composition in MRI with Convolutional Neural Networks
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Evaluating Deep Learning Algorithms in Pulmonary Nodule Detection
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MethodPulmonary Nodule Detection in CT Images Using Optimal Multilevel Thresholds and Rule-based Filtering
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Augmenting LIDC dataset using 3D generative adversarial networks to improve lung nodule detection
Gao, C., Clark, S. W., Furst, J. D., & Raicu, D. S. (2019) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2513011 · Cited by 17
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Uçar, M., & Uçar, E. (2019) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis.02.01.538249 · Cited by 17
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General Purpose (GenP) Bioimage Ensemble of Handcrafted and Learned Features with Data AugmentationPreprint
Nanni, L., Brahnam, S., Ghidoni, S., & Maguolo, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.08084 · Cited by 20
“This discovery has been exploited by bioimage researchers who have used CNN [25] or ensembles of CNNs [26] as feature extractors; the resulting learned features are then treated like SIFT and LBP and become the input to other types of classifiers, such as SVM.”
MethodTransfer Learning for Colonic Polyp Classification Using Off-the-Shelf CNN Features
Ribeiro, E. F., Uhl, A., Wimmer, G., & Häfner, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54057-3_1 · Cited by 16
A ℓ2, 1 norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD
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Transfer Learning by Deep Tuning of Pre-trained Networks for Pulmonary Nodule Detection
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MethodDeep Convolutional Neural Networks for Thyroid Tumor Grading using Ultrasound B-mode Images
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Multicontext 3D residual CNN for false positive reduction of pulmonary nodule detection
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MethodInfluentialNodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks
Venkatesan, N. J., Shin, D. R., & Nam, C. S. (2021) · Applied Sciences · doi:10.3390/app11062838 · Cited by 14
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MethodAutomatic Detection of Lung Nodules Using 3D Deep Convolutional Neural Networks
Fu, L., Ma, J., Chen, Y., Larsson, R., & Zhao, J. (2019) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-019-2084-4 · Cited by 13
Classification of Benign and Malignant Pulmonary Nodules Based on Deep Learning
Zhang, Y., Zhang, J., Zhao, L., Wei, X., & Zhang, Q. (2018) · International Conference on Information Science and Control Engineering · doi:10.1109/icisce.2018.00042 · Cited by 14
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BackgroundPlaque Recognition of Carotid Ultrasound Images Based on Deep Residual Network
Ma, W., Zhou, R., Zhao, Y., Xia, Y., Fenster, A., & Ding, M. (2019) · IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · doi:10.1109/itaic.2019.8785825 · Cited by 13
CAESAR: concept augmentation based semantic representation for cross-modal retrieval
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“Let (cid:7) = ˆ C 1 2 VV ˆ C VT ˆ C 1 2 T T , the optimal objective value is the sum of the top-k singular value of (cid:7) .”
BackgroundSoil micromorphological image classification using deep learning: The porosity parameter
Arnay, R., Hernández‐Aceituno, J., & Mallol, C. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107093 · Cited by 12
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MethodUsing deep learning for detecting gender in adult chest radiographs
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Lung Cancer Detection Using CT Images and CNN Algorithm
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Radiomics in Medical Imaging—Detection, Extraction and Segmentation
Tian, J., Dong, D., Liu, Z., Zang, Y., Wei, J., Song, J., Mu, W., Wang, S., & Zhou, M. (2018) · Intelligent systems reference library · doi:10.1007/978-3-319-68843-5_11 · Cited by 11
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Tumor Segmentation and Classification Using Machine Learning Approaches
Phan, T. C., & Hieu, L. T. (2024) · International Journal of Data Informatics and Intelligent Computing · doi:10.59461/ijdiic.v3i1.89 · Cited by 10
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BackgroundExtraction of Product Evaluation Factors with a Convolutional Neural Network and Transfer Learning
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“The former approach uses the outputs of one or more layers of a network trained on a different task to form a generic feature detector; a new shallow model is then trained using these features as target data [40,45].”
Method3D Inception Convolutional Neural Networks for Automatic Lung Nodule Detection
Chen, Z., Han, J., & Jia, Y. (2017) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci.2017.287 · Cited by 10
Extraction and prioritization of product attributes using an explainable neural network
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“In this approach, we use the outputs of one or more layers of a network trained on a different task as generic feature detectors and train a new shallow model on the basis of these features for the target data [4, 32].”
MethodImage Classification Using a Fully Convolutional Neural Network CNN
Lalaoui, L., Abdelhak, D., Abderrahmane, B., & Toufik, M. (2022) · Mathematical Modelling and Engineering Problems · doi:10.18280/mmep.090325 · Cited by 9
“Therefore, in 2016, an effective approach was proposed to learning a rotation invariant CNN model to improve object detection, which is achieved through the introduction and learning of a new rotation-invariant layer based on existing CNN architectures [13, 14].”
BackgroundFurst, J. D., Raicu, D. S., Mahesh, S. V., & Jenuwine, N. (2018) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2293918 · Cited by 9
“Various probability thresholds can be applied to obtain sets binary predictions (5), which we compare to ground truth locations of nodules to evaluate the success of our system (6).”
BackgroundMethodTransfer learning for fluence map prediction in adrenal stereotactic body radiation therapy
Wang, W., Sheng, Y., Palta, M., Czito, B. G., Willett, C. G., Yin, F.-F., Wu, Q., Ge, Y., & Wu, J. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac3c14 · Cited by 10
Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging
Shin, H.-C., Roth, H. R., Gao, M., Lü, L., Xu, Z., Nogues, I., Yao, J., Mollura, D. J., & Summers, R. M. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-42999-1_8 · Cited by 10
Lung Nodule Texture Detection and Classification Using 3D CNN
Harsono, I. W. (2019) · CommIT (Communication and Information Technology) Journal · doi:10.21512/commit.v13i2.5995 · Cited by 8
“There is no research about implementing 3D CNN to detect and classify lung nodule texture [1, 7–9, 14, 18, 19, 22– 24].”
BackgroundMethodKather, J. N., Bello-Cerezo, R., Maria, F. D., van Pelt, G. W., Mesker, W. E., Halama, N., & Bianconi, F. (2020) · Intelligent systems reference library · doi:10.1007/978-3-030-42750-4_3 · Cited by 9
Diagnosis of COVID-19 with a Deep Learning Approach on Chest CT Slices
Yener, F. M., & Oktay, A. B. (2020) · Medical Technologies Congress (TIPTEKNO) · doi:10.1109/tiptekno50054.2020.9299266 · Cited by 9
“ImageNet pre-trained models adapt successfully to the medical image tasks, not only saving time and computational power but also raising the performance metrics [11,16].”
MethodTwo-Stage Convolutional Neural Network Architecture for Lung Nodule DetectionPreprint
Cao, H., Liu, H., Song, E., Ma, G., Xu, X., Jin, R., Liu, T., & Hung, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.03445 · Cited by 9
“Within the CNN methods, the 2D CNN method has been widely used [30-38].”
MethodCOVID-19 diagnosis in CT images using CNN to extract features and multiple classifiers
Carvalho, E. D., Carvalho, E. D., Filho, A. O. D. C., de Sousa, A. D., & Rabêlo, R. D. A. L. (2020) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe50027.2020.00075 · Cited by 8
“A strategy commonly used in the literature is the use of CNN as a resource extractor of medical images [12], [13].”
MethodClassification of Chest Diseases from X-ray Images on the CheXpert Dataset
Saleem, H. N., Sheikh, U. U., & Khalid, S. N. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-0749-3_64 · Cited by 8
Quantifiable Interactivity of Malicious URLs and the Social Media Ecosystem
Lai, C.-M., Shiu, H.-J., & Chapman, J. W. (2020) · Electronics · doi:10.3390/electronics9122020 · Cited by 8
“Many voting behaviors are related to this effect; voters may or may not follow their own conscience to make a voting decision but may just follow the majority opinion [8].”
BackgroundZhu, L. (2017) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2017.05.019 · Cited by 8
Roth, H. R., Lü, L., Liu, J., Yao, J., Seff, A., Cherry, K. M., Kim, L., & Summers, R. M. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-42999-1_3 · Cited by 8
OCT-Based Periodontal Inspection Framework
Lai, Y., Chiu, C.-H., Cai, Z., Lin, J.-Y., Yao, C., Lyu, D.-Y., Lee, S., Chen, K.-W., & Chen, I.-Y. (2019) · Sensors · doi:10.3390/s19245496 · Cited by 7
“[29] combined a neural network and Support Vector Machine (SVM) to identify lung nodules.”
BackgroundPriyanka, & Kumar, D. (2020) · Recent Advances in Computer Science and Communications · doi:10.2174/2666255813999200915141534 · Cited by 7
Human Anomaly Detection in Surveillance Videos: A Review
Chidananda, K., & Kumar, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-0739-4_75 · Cited by 7
A Deep Bayesian Ensembling Framework for COVID-19 Detection using Chest CT Images
Tabarisaadi, P., Khosravi, A., & Nahavandi, S. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283003 · Cited by 7
“For instance [38], [39] used pretrained CNNs as feature extraction for chest pathology and in [40] , [41] the extracted features are used for pulmonary nodule detection.”
MethodMendez, M., Castillo, F., Probyn, L. J., Kras, S., & Tyrrell, P. N. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03309-6 · Cited by 6
“While CNNs have been applied extensively in radiology, pathology, and other medical domains [17–26], their adaptation to ultrasonography remains moderate due to the inherent challenges of ultrasound images, such aslowresolutionandoperator-dependentvariability[27–33].”
Tajbakhsh, N., & Suzuki, K. (2018) · Intelligent systems reference library · doi:10.1007/978-3-319-68843-5_2 · Cited by 5
A deep-learning toolkit for visualization and interpretation of segmented medical images
Ghosal, S., & Shah, P. P. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100107 · Cited by 5
“[8, 31, 32, 33, 23] For example, one study [34] claimed that use of pretrained weights for the segmentation of MRI images by fine-tuning the pretrained AlexNet CNN model to classify RGB fused images achieved better performance.”
BackgroundMulti-Perspective Hierarchical Deep-Fusion Learning Framework for Lung Nodule Classification
Şekeroğlu, K., & Soysal, Ö. M. (2022) · Sensors · doi:10.3390/s22228949 · Cited by 5
“Once the class scores from each CNN are computed, class scores are fused using a product rule on the output probabilities [22].”
MethodGainey, J. C., He, Y., Zhu, R., Baek, S., Wu, X., Buatti, J., Allen, B., Smith, B. J., & Kim, Y. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.868471 · Cited by 4
Classification of Pulmonary Nodule using New Transfer Method Approach
Gillani, S. W., & Ning, B. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110902 · Cited by 5
“[8] evaluated CNN as well as food over fat as a clinical CAD tool for the identification of lung cancer tumors.”
A Rapid, Nondestructive Method for Vascular Network Visualization
Veith, A. P., & Baker, A. B. (2020) · BioTechniques · doi:10.2144/btn-2020-0108 · Cited by 5
(2019) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.f8437.088619 · Cited by 5
Skin lesion classification based on hybrid self‐supervised pretext task
Yang, D., Zhang, J., Li, Y., & Ling, Z. (2024) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.23058 · Cited by 4
Segmenting Lungs from Whole-Body CT Scans
Walczak, M., Burda, I. M., Nalepa, J., & Kawulok, M. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-58274-0_32 · Cited by 4
Metadata
- Authors
- van Ginneken, B., Setio, A. A. A., Jacobs, C., & Ciompi, F.
- Affiliations
- Radboud University Medical Center (NL)
- Venue
- 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
- Publisher
- IEEE
- Published
- 2015-04
- Pages
- 286-289
- OpenAlex
- W1516595210
- Cited by
- 278
- References
- 13
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 11 · 2023: 9 · 2022: 17 · 2021: 29 · 2020: 48 · 2019: 51 · 2018: 39 · 2017: 43 · 2016: 20 · 2015: 3
- FWCI
- 19.72
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- lung nodule detection, convolutional neural network features, transfer learning, computed tomography, LIDC, volumetric medical data, feature extraction, support vector machine, object detection
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