ExplAIn: Explanatory Artificial Intelligence for Diabetic Retinopathy Diagnosis
Gwenol\'e Quellec, Hassan Al Hajj, Mathieu Lamard, Pierre-Henri Conze,, Pascale Massin, B\'eatrice Cochener

TL;DR
ExplAIn is an innovative AI framework that classifies diabetic retinopathy severity from fundus images while providing interpretable lesion-based explanations, matching black-box AI performance.
Contribution
This paper introduces ExplAIn, a novel end-to-end trainable XAI model that jointly performs classification and lesion segmentation for diabetic retinopathy diagnosis.
Findings
Achieves high classification accuracy comparable to black-box AI
Provides clear lesion-based explanations for diagnoses
Demonstrates effective lesion localization through self-supervision
Abstract
In recent years, Artificial Intelligence (AI) has proven its relevance for medical decision support. However, the "black-box" nature of successful AI algorithms still holds back their wide-spread deployment. In this paper, we describe an eXplanatory Artificial Intelligence (XAI) that reaches the same level of performance as black-box AI, for the task of classifying Diabetic Retinopathy (DR) severity using Color Fundus Photography (CFP). This algorithm, called ExplAIn, learns to segment and categorize lesions in images; the final image-level classification directly derives from these multivariate lesion segmentations. The novelty of this explanatory framework is that it is trained from end to end, with image supervision only, just like black-box AI algorithms: the concepts of lesions and lesion categories emerge by themselves. For improved lesion localization, foreground/background…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
