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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Image-based diagnostics in radiology demonstrate accuracy and efficacy rivalling specialty trained physicians.
“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]
AI technologies are likely to be incorporated into clinical practice.
“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]
To complement off-the-shelf CNN features, [10], [9], [12] all add and integrate various other hand-crafted image features as hybrid inputs for final CADe classification.
“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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Dong, Y. C., Pan, Y., Zhang, J., & Xu, W. (2017) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase.2017.59 · Cited by 77 · see citing works
“However, most recent CAD systems depends on the high resolution magnetic resonance imaging (MRI) or computed tomograph (CT) images, while ignoring the plain old X-rays that are much more widely utilized.”
BackgroundMethodInfluentialAutomated Pneumonia Diagnosis using a Customized Sequential Convolutional Neural Network
Siddiqi, R. (2019) · International Conference on Deep Learning Technologies (ICDLT) · doi:10.1145/3342999.3343001 · Cited by 78 · see citing works
Abidin, A. Z., Deng, B., DSouza, A. M., Nagarajan, M., Coan, P., & Wismüller, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.01.008 · Cited by 76 · see citing works
Lung Cancer Detection and Classification Based on Alexnet CNN
Agarwal, A., Patni, K., & Rajeswari, D. (2021) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces51350.2021.9489033 · Cited by 76 · see citing works
Towards radiologist-level cancer risk assessment in CT lung screening using deep learning
Trajanovski, S., Mavroeidis, D., Swisher, C. L., Gebre, B. G., Veeling, B. S., Wiemker, R., Klinder, T., Tahmasebi, A., Regis, S. M., Wald, C., et al. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101883 · Cited by 71 · see citing works
Building medical image classifiers with very limited data using segmentation networks
Wong, K. C. L., Syeda-Mahmood, T. F., & Moradi, M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.07.010 · Cited by 70 · see citing works
Bansal, G., Chamola, V., Narang, P., Kumar, S., & Raman, S. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.1164 · Cited by 67 · see citing works
“Studies [26, 39, 40] show XGBoost dominates classification of structured datasets.”
BackgroundMethodManzanera, O. M., Meles, S. K., Leenders, K. L., Renken, R. J., Pagani, M., Arnaldi, D., Nobili, F. M., Obeso, J. A., Rodríguez‐Oroz, M., Morbelli, S. D., et al. (2019) · International Journal of Neural Systems · doi:10.1142/s0129065719500102 · Cited by 63 · see citing works
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Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective
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“In biomedical imaging analyses, deep learning demonstrated impressive capabilities in thoraco-abdominal lymph node detection and interstitial lung disease classification [7], chest pathology identification [8, 9], real-time 2D/3D image registration [10], mammographic lesion detection and diagnosis [11], image segmenta…”
BackgroundA Hybrid CNN and RBF-Based SVM Approach for Breast Cancer Classification in Mammograms
Alkhaleefah, M., & Wu, C.-C. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00159 · Cited by 59 · see citing works
“Another work [6] used the features from a pre-trained OverFeat CNN that was trained for object detection in nat-ural images, for nodule detection in computed tomography scans.”
MethodMorphoCluster: Efficient Annotation of Plankton Images by Clustering
Schröder, S., Kiko, R., & Koch, R. (2020) · Sensors · doi:10.3390/s20113060 · Cited by 59 · see citing works
Ravishankar, H., Prabhu, S. M., Vaidya, V., & Singhal, N. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493382 · Cited by 58 · see citing works
“CNNs have also been recently explored for medical imaging tasks, such as detection of nodules in CTs [7] and landmark points in 3-D MRIs [8].”
MethodLiu, X., Fu, T., Pan, Z., Liu, D., Hu, W., Liu, J., & Zhang, K. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2856276 · Cited by 58 · see citing works
“Recent work [36], [37] suggested that hand-designed features and deep features are complementary and combining them can generate more powerful representation.”
BackgroundConvolutional neural networks for lung cancer screening in computed tomography (CT) scans
Rao, P., Pereira, N. A., & Srinivasan, R. (2016) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i.2016.7918014 · Cited by 56 · see citing works
“hand crafted features which are tailored to the particular task at hand [1, 2].”
BackgroundDeepLN: A framework for automatic lung nodule detection using multi-resolution CT screening images
Xu, X., Wang, C., Guo, J., Yang, L., Bai, H., Li, W., & Zhang, Y. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105128 · Cited by 56 · see citing works
Sharma, S., & Mehra, R. (2019) · The Visual Computer · doi:10.1007/s00371-019-01768-6 · Cited by 55 · see citing works
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Lung Nodule Detection in CT Images Using a Raw Patch-Based Convolutional Neural Network
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“According to the Ginneken’s work [31], it is an arduous task to classify adhesion pulmonary nodules attached lung wall because lung wall J Digit Imaging (2019) 32:971–979 972”
BackgroundAutomated lung cancer diagnosis using three-dimensional convolutional neural networks
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“In the case of deep learning strategies, few methods added convolutional neural networks (CNN) in addition to handcrafted features or use CNN to extract features and classify using a different methodology [5, 49].”
MethodQuality and content analysis of fundus images using deep learning
Chalakkal, R. J., Abdulla, W. H., & Thulaseedharan, S. S. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.03.019 · Cited by 47 · see citing works
Decision and feature level fusion of deep features extracted from public COVID-19 data-sets
İlhan, H. O., Serbes, G., & Aydın, N. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02945-8 · Cited by 49 · see citing works
“otential for having benefit from knowledge transfer in medical data sets. For instance, in [58], a pre-trained CNN was employed as a feature extractor with the aim of chest pathology identification. In [59], pre-trained CNN based features have shown improved performance as they were fused with traditional handcrafted fe…”
MethodGuo, Z., Zhao, L., Yuan, J., & Yu, H. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3131671 · Cited by 45 · see citing works
Gupta, A., Saar, T., Märtens, O., & Moullec, Y. L. (2018) · Medical Physics · doi:10.1002/mp.12746 · Cited by 45 · see citing works
Sun, L., Wang, Z., Pu, H., Yuan, G., Guo, L., Pu, T., & Peng, Z. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104357 · Cited by 45 · see citing works
Wang, W., & Chakraborty, G. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01990-z · Cited by 44 · see citing works
“For nodule detection, during 2015-16, there are works based on 2D images [8, 9], and more recently 3D images are used with higher sensitivity [10–14].”
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A novel biomedical image indexing and retrieval system via deep preference learning
Pang, S., Orgun, M. A., & Yu, Z. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.02.003 · Cited by 39 · see citing works
Image Restoration for Low-Dose CT via Transfer Learning and Residual Network
Zhong, A., Li, B., Luo, N., Xu, Y. X., Zhou, L., & Zhen, X. (2020) · IEEE Access · doi:10.1109/access.2020.3002534 · Cited by 39 · see citing works
“More-over, ImageNet-pretrained CNNs have been widely used for object detection and segmentation, such as chest pathology identification [39]–[42].”
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Ahn, E., Kumar, A., Kim, J., Li, C., Feng, D. D., & FULHAM, M. J. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493400 · Cited by 36 · see citing works
“The domain transferred CNNs (DT-CNNs), achieved good performance in image classification [9-11] because they learned generic image features from nonmedical images and then quantified those generic features within medical data.”
BackgroundFeature Representation Using Deep Autoencoder for Lung Nodule Image Classification
Mao, K., Tang, R., Wang, X., Zhang, W., & Wu, H. (2018) · Complexity · doi:10.1155/2018/3078374 · Cited by 36 · see citing works
Yin, X.-X., Hadjiloucas, S., Zhang, Y., & Tian, Z. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106510 · Cited by 36 · see citing works
Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network
Diniz, P. H. B., Valente, T. L. A., Diniz, J. O. B., Silva, A. C., Gattass, M., Ventura, N., Muniz, B. C., & Gasparetto, E. L. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.011 · Cited by 34 · see citing works
Image-based surrogate biomarkers for molecular subtypes of colorectal cancer
Popovici, V. C., Budínská, E., Dušek, L., Kozubek, M., & Bosman, F. T. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx027 · Cited by 33 · see citing works
Fu, L., Ma, J., Ren, Y., Han, Y. S., & Zhao, J. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2253995 · Cited by 33 · see citing works
- 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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