Abstract
While deep networks have demonstrated state-of-the-art performance in medical image analysis, they suffer from biases caused by undesirable confounding variables (e.g., sex, age, race). Traditional statistical methods for removing confounders are often incompatible with modern deep networks. To address this challenge, we introduce a novel learning framework, named ReConfirm, based on the invariant risk minimization (IRM) theory to eliminate the biases caused by confounding variables and make deep networks more robust. Our approach allows end-to-end model training while capturing causal features responsible for pathological findings instead of spurious correlations. We evaluate our approach on NIH chest X-ray classification tasks where sex and age are confounders.
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This research was funded by the National Science Foundation (1910973).
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Zare, S., Nguyen, H.V. (2022). Removal of Confounders via Invariant Risk Minimization for Medical Diagnosis. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13438. Springer, Cham. https://doi.org/10.1007/978-3-031-16452-1_55
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