Abstract
Nonalcoholic fatty liver disease (NAFLD) has become one of the most common liver diseases. Image analysis of liver biopsy is the gold standard for early diagnosis of NAFLD. Deep learning offers an effective tool to evaluate NAFLD with histological feature scoring on ballooning, inflammation, steatosis, and fibrosis. However, current methods using convolutional neural networks (CNNs) may not extract multi-scale and contextual information of histological images effectively. For better performance, we introduce a Swin-Transformer-based model using deep self-supervision with the residual module. To the best of our knowledge, it is the first time Swin Transformer has been used in liver pathology. The whole slide images (WSIs) were cropped into patches at two scales and analyzed for four features. Experiments conducted on the public dataset Biopsy4Grading and the in-house dataset Steatosis-Biopsy indicate that our method achieves superior classification performance to previous CNN-based methods in this field.
R. Yan and Q. He—Contribute equally to this work.
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Acknowledgements
This work was approved by the Ethics Committees of Third People’s Hospital of Shenzhen (No. 2021-028), and supported by Special project of Clinical toxicology, Chinese Society of Toxicology under Grant CST2020CT102.
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Yan, R. et al. (2022). DEST: Deep Enhanced Swin Transformer Toward Better Scoring for NAFLD. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13535. Springer, Cham. https://doi.org/10.1007/978-3-031-18910-4_17
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