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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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
“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]
Citations
Cites
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
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
The National Lung Screening Trial Research Team (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1102873 · Cited by 11,064
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451
An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A. Y., & Lee, H. (2011) · Cited by 2,760
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680
CNN Features off-the-shelf: an Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · arXiv · doi:10.48550/arxiv.1403.6382 · Cited by 607
Roth, H. R., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., & Summers, R. M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10404-1_65 · Cited by 355
Computer-aided Diagnosis: How to Move from the Laboratory to the Clinic
van Ginneken, B., Schaefer-Prokop, C. M., & Prokop, M. (2011) · Radiology · doi:10.1148/radiol.11091710 · Cited by 306
van Ginneken, B., Armato, S. G., de Hoop, B., de Vorst, S. V. A.-V., Duindam, T., Niemeijer, M., Murphy, K., Schilham, A. M. R., Retico, A., Fantacci, M. E., et al. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.05.005 · Cited by 298
Research works printed but not linked: 2
- jacobs, 2013, Benchmarking computer-aided detection of pulmonary nodules on the recently completed publicly available LIDC/IDRI database, Radiological Society of North America Annual Meeting
- krizhevsky, 2012, Ima-genet classification with deep convolutional neural net-works, NIPS, 1097
Cited by
Sorted by citations
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Self-citationShin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277
“This analysis is in contrast to [28], [29], who provided only”
ResultArtificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817
Greenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,725
“[27] integration of CNN-based features with handcrafted features enabled improved performance in a nodule detection system.”
Method
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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