Learning from Everyday Images Enables Expert-like Diagnosis of Retinal Diseases

Cell

Volumn 172, Issue 5, 2018, Pages 893-895

  Rampasek, Ladislav ^a,b,c   Goldenberg, Anna ^a,b,c  

^a UNIVERSITY OF TORONTO   (Canada)

^b HOSPITAL FOR SICK CHILDREN   (Canada)

^c Vector Institute   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; COMPUTER ASSISTED TOMOGRAPHY; DIABETIC MACULAR EDEMA; DIAGNOSTIC IMAGING; DIGITAL IMAGING; IMAGE ANALYSIS; IMAGE PROCESSING; MACHINE LEARNING; NUCLEAR MAGNETIC RESONANCE IMAGING; OPHTHALMOLOGIST; OPTICAL COHERENCE TOMOGRAPHY; OPTICAL TOMOGRAPHY; PRIORITY JOURNAL; RETINA DISEASE; RETINA NEOVASCULARIZATION; SHORT SURVEY; HUMAN;

DEEP LEARNING; HUMANS; NEURAL NETWORKS (COMPUTER); RETINAL DISEASES;

EID: 85042359360     PISSN: 00928674     EISSN: 10974172     Source Type: Journal    
DOI: 10.1016/j.cell.2018.02.013     Document Type: Short Survey

References (8)

1. Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S., Dermatologist-level classification of skin cancer with deep neural networks. Nature 542 (2017), 115–118.
2. Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316 (2016), 2402–2410.
3. Kermany, D.S., Goldbaum, M., Cai, W., Valentim, C.C.S., Liang, H., Baxter, S.L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell 172 (2018), 1122–1131 this issue.
4. Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., and Greenspan, H. (2015). Chest pathology detection using deep learning with non-medical training. In 2015 IEEE 12th International Symposium, pp. 294–297.
5. 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., and Sánchez, C.I. (2017). A Survey on Deep Learning in Medical Image Analysis. arXiv, arXiv:1702.05747, https://arxiv.org/abs/1702.05747.
6. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255.
7. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826.
8. Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks? In NIPS’14 Proceedings of the 27th International Conference on Neural Information Processing Systems 2, 3320–3328.