In The Journal of pathology
Colorectal adenoma is a recognized precancerous lesion of colorectal cancer (CRC), and at least 80% of colorectal cancers are malignantly transformed from it. Therefore, it is essential to distinguish benign from malignant adenomas in the early screening of colorectal cancer. Many deep learning computational pathology studies based on whole slide images (WSIs) have been proposed. Most approaches require manual annotation of lesion regions on WSIs, which is time-consuming and labor-intensive. This study proposes a new approach MIST - Multiple Instance learning network based on Swin Transformer, which can accurately classify colorectal adenoma WSIs only with slide-level labels. MIST uses Swin Transformer as the backbone to extract features of images through self-supervised contrastive learning and uses a dual-stream multiple instance learning network to predict the class of slides. We trained and validated MIST on 666 WSIs collected from 480 colorectal adenoma patients in the Department of Pathology, the Affiliated Drum Tower Hospital of Nanjing University Medical School. These slides contained six common types of colorectal adenomas. The accuracy of external validation on 273 newly-collected WSIs from Nanjing First Hospital was 0.784, which was superior to the existing methods and reached a level comparable to that of the local pathologist's accuracy of 0.806. Finally, we analyzed the interpretability of MIST and observed that the lesion areas of interest in MIST were generally consistent with local pathologists. In conclusion, MIST is a low-burden, interpretable and effective approach that can be used in colorectal cancer screening and potential reduction of the mortality of CRC patients by assisting clinicians in the decision-making process. This article is protected by copyright. All rights reserved.
Cai Hongbin, Feng Xiaobing, Yin Ruomeng, Zhao Youcai, Guo Lingchuan, Fan Xiangshan, Liao Jun
2022-Nov-01
Classification, Colorectal adenoma, Pathology, Swin Transformer, Whole slide image
