Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists.

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 t

SUBMITTER: Song Z 

PROVIDER: S-EPMC7485250 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature