ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
This work addresses the need for interpretable classification in neuroscience, where understanding discriminative features is crucial for modeling behaviors or diseases, though it appears incremental as it builds on existing FA and image-to-image translation techniques.
The authors tackled the challenge of generating accurate feature attribution maps for brain image classification by developing a VAE-GAN framework that disentangles class-relevant features from background variation, resulting in improved performance over baseline methods on datasets like ADNI and UK Biobank.
Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification problems but is particularly crucial within the neuroscience domain, where accurate mechanistic models of behaviours, or disease, require knowledge of all features discriminative of a trait. At the same time, predicting class relevance from brain images is challenging as phenotypes are typically heterogeneous, and changes occur against a background of significant natural variation. Here, we present a novel framework for creating class specific FA maps through image-to-image translation. We propose the use of a VAE-GAN to explicitly disentangle class relevance from background features for improved interpretability properties, which results in meaningful FA maps. We validate our method on 2D and 3D brain image datasets of dementia (ADNI dataset), ageing (UK Biobank), and (simulated) lesion detection. We show that FA maps generated by our method outperform baseline FA methods when validated against ground truth. More significantly, our approach is the first to use latent space sampling to support exploration of phenotype variation. Our code will be available online at https://github.com/CherBass/ICAM.