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Thomas Zeng

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Proceedings Article | 7 April 2023 Presentation + Paper

No nodule left behind: evaluating lung nodule malignancy classification with different stratification schemes

Thomas Zeng, Elias Furst, Yiyang Wang, Roselyne Tchoua, Jacob Furst, Daniela Raicu

Proceedings Volume 12465, 124651O (2023) https://doi.org/10.1117/12.2653633

KEYWORDS: Education and training, Lung, Tumor growth modeling, Machine learning, Computer aided detection, Visualization, Classification systems, Data modeling, Semantics, Performance modeling

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Machine learning models have been widely used in lung cancer computer-aided diagnosis (CAD) studies. However, the heterogeneity in the visual appearance of lung nodules as well as lack of consideration of hidden subgroups in the data are significant obstacles to generating accurate CAD outcomes across all nodule instances. Previous lung cancer CAD models aim to achieve Empirical Risk Minimization (ERM), which leads to a high overall accuracy but often fails at predicting certain subgroups caused by the lung cancer heterogeneity. In this study, we aim to discover hidden lung nodule subgroups and enhance the malignancy classification performance of the worst-performance subgroup when compared to traditional ERM methods. We experiment with three different stratification methods for lung nodule subgroup discovery: spiculation-based, clustering-based, and malignancybased. A high overlap between subgroup labels generated from the clustering-based approach and labels obtained from radiologists’ semantic annotations indicates our discovered subgroups are semantically meaningful. We successfully improved the worst malignancy classification performance lung nodule subgroup through utilizing Group Distributionally Robust Optimization (gDRO) when compared to ERM as a baseline. Our study creates a framework for augmenting lung nodule malignancy classification under domain shift situations caused by the disease heterogeneity and underscores the necessity of addressing hidden stratification for future CAD schemes.

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