CAF-AHGCN: context-aware attention fusion adaptive hypergraph convolutional network for human-interpretable prediction of gigapixel whole-slide image
-
Published:2024-02-13
Issue:
Volume:
Page:
-
ISSN:0178-2789
-
Container-title:The Visual Computer
-
language:en
-
Short-container-title:Vis Comput
Author:
Liang MeiyanORCID, Jiang Xing, Cao Jie, Li Bo, Wang Lin, Chen Qinghui, Zhang Cunlin, Zhao Yuejin
Funder
Natural Science Foundation of Shanxi Province Young Scientists Fund
Publisher
Springer Science and Business Media LLC
Reference51 articles.
1. Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147–171 (2009) 2. Marini, N., Marchesin, S., Otálora, S., Wodzinski, M., Caputo, A., Van Rijthoven, M., Aswolinskiy, W., Bokhorst, J.-M., Podareanu, D., Petters, E., et al.: Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations. NPJ Digital Med 5, 1–18 (2022) 3. Wu, J., Zhang, R., Gong, T., Bao, X., Gao, Z., Zhang, H., Wang, C., Li, C.: A precision diagnostic framework of renal cell carcinoma on whole-slide images using deep learning. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, pp 2104–2111 (2021). 4. Sankarapandian, S., Kohn, S., Spurrier, V., Grullon, S., Soans, R.E., Ayyagari, K.D., Chamarthi, R.V., Motaparthi, K., Lee, J.B., Shon, W., et al.: A pathology deep learning system capable of triage of melanoma specimens utilizing dermatopathologist consensus as ground truth. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp 629–638 (2021). 5. Hsu, W.-W., Wu, Y., Hao, C., Hou, Y.-L., Gao, X., Shao, Y., Zhang, X., He, T., Tai, Y.: A computer-aided diagnosis system for breast pathology: a deep learning approach with model interpretability from pathological perspective. arXiv preprint arXiv:210802656 (2021).
|
|