Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencoder
This work addresses the need for accurate and interpretable models in medical imaging, specifically for Alzheimer's Disease diagnosis, though it appears incremental as it builds on existing methods like contrastive loss and diffusion autoencoders.
The paper tackles Alzheimer's Disease classification from 2D MRI images by developing an interpretable deep learning model that compares images to prototypical examples in a latent space, achieving classification accuracy comparable to black-box approaches.
In visual object classification, humans often justify their choices by comparing objects to prototypical examples within that class. We may therefore increase the interpretability of deep learning models by imbuing them with a similar style of reasoning. In this work, we apply this principle by classifying Alzheimer's Disease based on the similarity of images to training examples within the latent space. We use a contrastive loss combined with a diffusion autoencoder backbone, to produce a semantically meaningful latent space, such that neighbouring latents have similar image-level features. We achieve a classification accuracy comparable to black box approaches on a dataset of 2D MRI images, whilst producing human interpretable model explanations. Therefore, this work stands as a contribution to the pertinent development of accurate and interpretable deep learning within medical imaging.