Trusted multi-scale classification framework for whole slide image-Reference-Cited by-同舟云学术

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Trusted multi-scale classification framework for whole slide image

Published:2024-03 Issue: Volume:89 Page:105790
ISSN:1746-8094
Container-title:Biomedical Signal Processing and Control
language:en
Short-container-title:Biomedical Signal Processing and Control

Author:

Feng Ming,Xu Kele,Wu Nanhui,Huang Weiquan,Bai Yan,Wang Yin,Wang Changjian,Wang Huaimin

Funder

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Health Informatics,Signal Processing,Biomedical Engineering

Reference35 articles.

1. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images;Campanella;Nat. Med.,2019

2. Weakly supervised deep learning for whole slide lung cancer image analysis;Wang;IEEE Trans. Cybern.,2019

3. Classification of breast cancer histology images using convolutional neural networks;Araújo;PLoS One,2017

4. N. Hashimoto, D. Fukushima, R. Koga, Y. Takagi, K. Ko, K. Kohno, M. Nakaguro, S. Nakamura, H. Hontani, I. Takeuchi, Multi-scale domain-adversarial multiple-instance cnn for cancer subtype classification with unannotated histopathological images, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 3852–3861.

5. S. Maksoud, K. Zhao, P. Hobson, A. Jennings, B.C. Lovell, Sos: Selective objective switch for rapid immunofluorescence whole slide image classification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 3862–3871.

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