Paper on inherently interpretable multi-label classifiers using counterfactuals accepted to MIDL 2023!
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Our research on using class-specific counterfactuals to create an inherently interpretable multi-label classifier led by MLMIA lab member Susu Sun has been accepted to the Medical Imaging with Deep Learning (MIDL) conference, which will be held in Nashville, Tennessee this year.
A pre-print can be found here: arxiv.org/abs/2303.00500
More information can be found in this Twitter thread.