Generative and contrastive based self-supervised learning model for histopathology image analysis
Authors: 
Hongbo Chu, Fang Li, Yonghong He, and Tian GuanAuthors Info & Claims
Hongbo Chu, Fang Li, Yonghong He, and Tian GuanAuthors Info & ClaimsFebruary 2023
Pages 354 - 360
Abstract
In recent years, the deep neural network has achieved astonishing results in the automatic Whole slide images(WSI) processing. The current mainstream approach is inseparable from a large number of manual annotations. However, labeling such giant images with billions of pixels is very labor-intensive. So the shortage of annotation has become a bottleneck in developing Whole slide image diagnostic models. Therefore, we propose a new self-supervised learning(SSL) network to solve the problem of insufficient annotation. In our work, massive semantic information can be extracted from a large number of WSI, which significantly gets rid of our dependence on the label. At the same time, the results are further refined by a silhouette-coefficient-based recursive Spectral Clustering Bipartition, which significantly improved the classification accuracy. Moreover, our framework is highly transferable and can take on many downstream tasks in pathology. Our final results are verified on the NCT-CRC-100K and MSHIR datasets. Our code is available at https://github.com/Hongbo-Chu/generative-contrastive
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Index Terms
- Generative and contrastive based self-supervised learning model for histopathology image analysis
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February 2023
619 pages
ISBN:9781450398411
DOI:10.1145/3587716
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Published: 07 September 2023
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ICMLC 2023
ICMLC 2023: 2023 15th International Conference on Machine Learning and Computing
February 17 - 20, 2023
Zhuhai, China
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