Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists. Issue 9 (10th September 2020)

• Record Type: Journal Article
• Authors: Song, Zhigang; Yu, Chunkai; Zou, Shuangmei; Wang, Wenmiao; Huang, Yong; Ding, Xiaohui; Liu, Jinhong; Shao, Liwei; Yuan, Jing; Gou, Xiangnan; Jin, Wei; Wang, Zhanbo; Chen, Xin; Chen, Huang; Liu, Cancheng; Xu, Gang; Sun, Zhuo; Ku, Calvin; Zhang, Yongqiang; Dong, Xianghui; Wang, Shuhao; Xu, Wei; Lv, Ning; Shi, Huaiyin
• Is Part Of: BMJ open. Volume 10:Issue 9(2020)
• Publication Date: 2020-09-10
• Subjects: computational pathology -- model interpretability, colorectal adenoma -- digital pathology -- deep learning; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2019-036423
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: The microscopic evaluation of slides has been gradually moving towards all digital in recent years, leading to the possibility for computer-aided diagnosis. It is worthwhile to know the similarities between deep learning models and pathologists before we put them into practical scenarios. The simple criteria of colorectal adenoma diagnosis make it to be a perfect testbed for this study. Design: The deep learning model was trained by 177 accurately labelled training slides (156 with adenoma). The detailed labelling was performed on a self-developed annotation system based on iPad. We built the model based on DeepLab v2 with ResNet-34. The model performance was tested on 194 test slides and compared with five pathologists. Furthermore, the generalisation ability of the learning model was tested by extra 168 slides (111 with adenoma) collected from two other hospitals. Results: The deep learning model achieved an area under the curve of 0.92 and obtained a slide-level accuracy of over 90% on slides from two other hospitals. The performance was on par with the performance of experienced pathologists, exceeding the average pathologist. By investigating the feature maps and cases misdiagnosed by the model, we found the concordance of thinking process in diagnosis between the deep learning model and pathologists. Conclusions: The deep learning model for colorectal adenoma diagnosis is quite similar to pathologists. It is on-par with pathologists' performance, makes similar mistakes and learns rational reasoning logics. Meanwhile, it obtains high accuracy on slides collected from different hospitals with significant staining configuration variations.