Open AccessUpper gastrointestinal anatomy detection with multi‐task convolutional neural networks
Author(s) -
Xu Zhang,
Tao Yu,
Wenfang Zheng,
Ne Lin,
Zhengxing Huang,
Jiquan Liu,
Weiling Hu,
Huilong Duan,
Jianmin Si
Publication year - 2019
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2019.0066
Subject(s) - computer science , esophagogastroduodenoscopy , convolutional neural network , artificial intelligence , task (project management) , process (computing) , pattern recognition (psychology) , computer vision , video quality , image quality , image (mathematics) , radiology , medicine , endoscopy , management , economics , operating system , metric (unit) , operations management
Esophagogastroduodenoscopy (EGD) has been widely applied for gastrointestinal (GI) examinations. However, there is a lack of mature technology to evaluate the quality of the EGD inspection process. In this Letter, the authors design a multi‐task anatomy detection convolutional neural network (MT‐AD‐CNN) to evaluate the EGD inspection quality by combining the detection task of the upper digestive tract with ten anatomical structures and the classification task of informative video frames. The authors’ model is able to eliminate non‐informative frames of the gastroscopic videos and detect the anatomies in real time. Specifically, a sub‐branch is added to the detection network to classify NBI images, informative and non‐informative images. By doing so, the detected box will be only displayed on the informative frames, which can reduce the false‐positive rate. They can determine the video frames on which each anatomical location is effectively examined, so that they can analyse the diagnosis quality. Their method reaches the performance of 93.74% mean average precision for the detection task and 98.77% accuracy for the classification task. Their model can reflect the detailed circumstance of the gastroscopy examination process, which shows application potential in improving the quality of examinations.