Graph interaction for automated diagnosis of thoracic disease using x-ray images

Bumjun Jung; Lin Gu; Tatsuya Harada

doi:10.1117/12.2612707

4 April 2022 Graph interaction for automated diagnosis of thoracic disease using x-ray images

Bumjun Jung, Lin Gu, Tatsuya Harada

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120320L (2022) https://doi.org/10.1117/12.2612707
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Deep learning has shown successful performance not only in supervised disease detection but also lesion localization under the weakly supervised learning framework with medical image processing. However, few consider the semantic relationship among the diseases and lesions which plays a critical role in actual clinical diagnosis. In this work, we propose a novel framework: Feature map Graph Representational Probabilistic Class Activation Map (FGR-PCAM) to learn the graph structure of lesion-specific features and consider these relationships while also leveraging the localization ability of PCAM. Considering the relations of localized lesion-specific features has been shown to enhance both thoracic diseases classification and localization tasks on CheXpert and Chest Xray14 datasets. Accurate classification and localization of Chest X-ray images would also help us fight against the COVID-19 and unveil COVID-19 fingerprints.

Conference Presentation

Citation Download Citation

Bumjun Jung, Lin Gu, and Tatsuya Harada "Graph interaction for automated diagnosis of thoracic disease using x-ray images", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120320L (4 April 2022); https://doi.org/10.1117/12.2612707

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