An automatic diagnosis of idiopathic pulmonary fibrosis (IPF) using domain knowledge-guided attention models in HRCT images

Wenxi Yu; Hua Zhou; Youngwon Choi; Jonathan G. Goldin; Pangyu Teng; Grace Hyun J. Kim

doi:10.1117/12.2579735

15 February 2021 An automatic diagnosis of idiopathic pulmonary fibrosis (IPF) using domain knowledge-guided attention models in HRCT images

Wenxi Yu, Hua Zhou, Youngwon Choi, Jonathan G. Goldin, Pangyu Teng, Grace Hyun J. Kim

Author Affiliations +

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115971Y (2021) https://doi.org/10.1117/12.2579735
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Two major challenges hinder the deployment of deep learning-based systems into clinical practice: the need for numerous high-quality well-labeled data and the lack of explainability. Attention models, originated from natural language processing, have been popular to address the label scarcity problem and encourage model explainability. In this work, we developed a domain knowledge-guided attention model for disease diagnosis with only coarse scan-level labels and the population-level domain knowledge. The use of guided attention models encourages the deep learning-based diagnosis model to focus on the area of interests in an end-to-end manner. The research interest is to diagnose subjects with idiopathic pulmonary fibrosis (IPF) among subjects with interstitial lung disease (ILD) using an axial chest high resolution computed tomography (HRCT) scan. Our dataset contains 279 IPF patients and 423 non-IPF ILD patients. The network’s performance was evaluated by the area under the receiver operating characteristic curve (AUC). We observe that without attention-based loss function, the IPF diagnosis model reaches satisfactory performance (AUC=0.972), but lack explainability; when increasing the relative importance of attention-based loss, the IPF diagnosis model increases performance (AUC=0.988), along with the model explainability. Our contributions are (1) developing an IPF diagnosis model that only uses scan-level weak supervision; (2) incorporating population-level domain knowledge into the training of IPF diagnosis model in an end-to-end manner; (3) enhancing the explainability of deep learning systems by introducing attention mechanisms.

Conference Presentation

Citation Download Citation

Wenxi Yu, Hua Zhou, Youngwon Choi, Jonathan G. Goldin, Pangyu Teng, and Grace Hyun J. Kim "An automatic diagnosis of idiopathic pulmonary fibrosis (IPF) using domain knowledge-guided attention models in HRCT images", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115971Y (15 February 2021); https://doi.org/10.1117/12.2579735

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