Deep Learning in Surgical Assistance
Author:
Affiliation:
1. Graduate School of Informatics, Nagoya University
Publisher
The Japan Society of Computer Aided Surgery
Link
https://www.jstage.jst.go.jp/article/jscas/22/1/22_54/_pdf
Reference12 articles.
1. 1) Roth HR, Oda H, Zhou X, et al. An application of cascaded 3D fully convolutional networks for medical image segmentation. Comput Med Imaging Graph 2018; 66: 90-99.
2. 2) Shen C, Milletari F, Roth HR, et al. Multi-class abdominal organs segmentation with improved V-Nets. Proc. SPIE Med Imag 2019; 10949: 109490B-1-7.
3. 3) Zhang Y, Chung ACS. Deep supervision with additional labels for retinal vessel segmentation task. MICCAI 2018; 11071: 83-91.
4. 4) Wu Y, Xia Y, Song Y, et al. Multiscale network followed network model for retinal vessel segmentation. MICCAI 2018; 11071: 119-126.
5. 5) Twinanda AP, Shehata S, Mutter D, et al. EndoNet: A deep architecture for recognition tasks on laparoscopic videos. IEEE Trans. Med. Imag 2016; 36 (1): 86-97.
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