An adversarial approach for the robust classification of pneumonia from chest radiographs

By Joseph D. Janizek, Gabriel Erion-Barner, Alex J. DeGrave, Su-In Lee. 2020

33 citations recorded by the source corpus.

While deep learning has shown promise in the domain of disease classification from medical images, models based on state-of-the-art convolutional neural network architectures often exhibit performance loss due to dataset shift. Models trained using data from one hospital system achieve high predictive performance when tested on data from the same hospital, but perform significantly worse when they are tested in different hospital systems. Furthermore, even within a given hospital system, deep learning models have been shown to depend on hospital- and patient-level confounders rather than meaningful pathology to make classifications. In order for these models to be safely deployed, we would like to ensure that they do not use confounding variables to make their classification, and that they will work well even when tested on images from hospitals that were not included in the training data. We attempt to address this problem in the context of pneumonia classification from chest radiographs. We propose an approach based on adversarial optimization, which allows us to learn more robust models that do not depend on confounders. Specifically, we demonstrate improved out-of-hospital generalization performance of a pneumonia classifier by training a model that is invariant to the view position of chest radiographs (anterior-posterior vs. posterior-anterior). Our approach leads to better predictive performance on external hospital data than both a standard baseline and previously proposed methods to handle confounding, and also suggests a method for identifying models that may rely on confounders.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

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Su-In Lee

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Citations in the corpus

Improving the accuracy of medical diagnosis with causal machine learning (2020, background): “The question of how to incorporate causal and counter-factual reasoning into other machine learning methods beyond structural causal models, for example in Deep Learning for image classi ﬁ cation 82,83 and deep generative models 84 – 86 , is an active area research.”
AI for radiographic COVID-19 detection selects shortcuts over signal (2020, methodology): “Forcing models to first learn general image features should also serve as an inductive bias to prevent overfitting on domain-specific features [24].”