Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization.

ABSTRACT: The success of deep learning in identifying complex patterns exceeding human intuition comes at the cost of interpretability. Non-linear entanglement of image features makes deep learning a "black box" lacking human meaningful explanations for the models' decision. We present DISCOVER, a generative model designed to discover the underlying visual properties driving image-based classification models. DISCOVER learns disentangled latent representations, where each latent feature encodes a unique classification-driving visual property. This design enables "human-in-the-loop" interpretation by generating disentangled exaggerated counterfactual explanations. We apply DISCOVER to interpret classification of in vitro fertilization embryo morphology quality. We quantitatively and systematically co

SUBMITTER: Rotem O 

PROVIDER: S-EPMC11349992 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature