The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Deep Learning for Medical Image Processing: Overview, Challenges and the Future
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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
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
Shin, 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
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · cited 3×
“Alban [8] de-noised the angiograph images of EyePACS and then applied CNNs for detection of DR.”
MethodAn analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A. Y., & Lee, H. (2011) · Cited by 2,758
Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R. J., Cree, I. A., & Rajpoot, N. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2525803 · Cited by 1,224
Age and gender classification using convolutional neural networks
Levi, G., & Hassncer, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301352 · Cited by 1,151
THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY
Heath, M. D., Bowyer, K. W., Kopans, D. B., & Moore, R. M. (2007) · Cited by 1,017
Convolutional Neural Networks for Diabetic Retinopathy
Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.07.014 · Cited by 988
Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis
Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968
Cited by
Artificial intelligence for mental health and mental illnesses: an overview
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1094-0 · Cited by 970
Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-González, J., Routier, A., Bottani, S., Dormont, D., Durrleman, S., Burgos, N., & Colliot, O. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101694 · Cited by 809
Maghdid, H., Asaad, A. T., Ghafoor, K. Z. G., Sadiq, A. S., Mirjalili, S., & Khan, M. K. K. (2021) · Multimodal Image Exploitation and Learning 2021 · doi:10.1117/12.2588672 · Cited by 459
Pareek, P., & Thakkar, A. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09904-8 · Cited by 455
VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations
Nguyen, H. Q., Lam, K., Le, L. T., Pham, H. H., Tran, D. Q., Nguyen, D. B., Le, D. D., Pham, C. M., Tong, H. T. T., Dinh, D. H., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01498-w · Cited by 435
Metadata
- Authors
- Razzak, M. I., Naz, S., & Zaib, A.
- Venue
- Lecture Notes in Computational Vision and Biomechanics
- Publisher
- Springer International Publishing
- Published
- 2017-11-14
- Pages
- 323-350
- ISSN
- 2212-9391 (ISSN-L)
- 2212-9413
- OpenAlex
- W2607941059
- Cited by
- 1,248
- References
- 48
- FWCI
- 141.75
- Type
- book chapter
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Deep Learning for Medical Image Processing: Overview, Challenges and the Future. https://app.cochat.ai/papers/10.1007/978-3-319-65981-7_12
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- Razzak, M. I., Naz, S., & Zaib, A. (2018). Deep Learning for Medical Image Processing: Overview, Challenges and the Future. Lecture Notes in Computational Vision and Biomechanics. https://doi.org/10.1007/978-3-319-65981-7_12
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
