  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. Code available at
https://github.com/suinleelab/cxr_adv.

Machine Learning;Image and Video Processing;Machine Learning