Prof. Hua Zhou Profile

Prof. Hua Zhou

at Univ of California Los Angeles

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

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

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

Proceedings Volume 11597, 115971Y (2021) https://doi.org/10.1117/12.2579735

KEYWORDS: Systems modeling, Pulmonary fibrosis, Performance modeling, Receivers, Lung, Diagnostics, Computed tomography

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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.

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