Classification of chest X-ray images by incorporation of medical domain knowledge into operation branch networks

BMC Medical Imaging

Research

Shinobu Kumagai¹  Kenya Kusunose²  Masataka Sata²  Kenshiro Shiraishi³  Takumasa Tsuji⁴  Jun’ichi Kotoku⁵  Yukina Hirata⁶ 

[1] Central Radiology Division, Teikyo University Hospital, 2-11-1 Kaga, Itabashi-Ku, 173-8606, Tokyo, Japan;Department of Cardiovascular Medicine, Tokushima University Hospital, 2-50-1, Kuramoto, Tokushima, Japan;Department of Radiology, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-Ku, 173-8605, Tokyo, Japan;Graduate School of Medical Care and Technology, Teikyo University, 2-11-1 Kaga, Itabashi-Ku, 173-8605, Tokyo, Japan;Graduate School of Medical Care and Technology, Teikyo University, 2-11-1 Kaga, Itabashi-Ku, 173-8605, Tokyo, Japan;Central Radiology Division, Teikyo University Hospital, 2-11-1 Kaga, Itabashi-Ku, 173-8606, Tokyo, Japan;Ultrasound Examination Center, Tokushima University Hospital, 2-50-1, Kuramoto, Tokushima, Japan;

关键词: Attention mechanism;    Chest X-ray images;    Convolutional neural networks;    Deep learning;    Explainable AI;   

DOI  :  10.1186/s12880-023-01019-0

 received in 2022-08-23, accepted in 2023-05-02,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThis study was conducted to alleviate a common difficulty in chest X-ray image diagnosis: The attention region in a convolutional neural network (CNN) does not often match the doctor’s point of focus. The method presented herein, which guides the area of attention in CNN to a medically plausible region, can thereby improve diagnostic capabilities.MethodsThe model is based on an attention branch network, which has excellent interpretability of the classification model. This model has an additional new operation branch that guides the attention region to the lung field and heart in chest X-ray images. We also used three chest X-ray image datasets (Teikyo, Tokushima, and ChestX-ray14) to evaluate the CNN attention area of interest in these fields. Additionally, after devising a quantitative method of evaluating improvement of a CNN’s region of interest, we applied it to evaluation of the proposed model.ResultsOperation branch networks maintain or improve the area under the curve to a greater degree than conventional CNNs do. Furthermore, the network better emphasizes reasonable anatomical parts in chest X-ray images.ConclusionsThe proposed network better emphasizes the reasonable anatomical parts in chest X-ray images. This method can enhance capabilities for image interpretation based on judgment.

【 授权许可】

CC BY   
© The Author(s) 2023

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