ExplAIn: Explanatory Artificial Intelligence for Diabetic Retinopathy Diagnosis

no code implementations • 13 Aug 2020 • Gwenolé Quellec, Hassan Al Hajj, Mathieu Lamard, Pierre-Henri Conze, Pascale Massin, Béatrice Cochener

In recent years, Artificial Intelligence (AI) has proven its relevance for medical decision support.

Explainable Artificial Intelligence (XAI)

Automatic detection of rare pathologies in fundus photographs using few-shot learning

no code implementations • 22 Jul 2019 • Gwenolé Quellec, Mathieu Lamard, Pierre-Henri Conze, Pascale Massin, Béatrice Cochener

This paper presents a new few-shot learning framework that extends convolutional neural networks (CNNs), trained for frequent conditions, with an unsupervised probabilistic model for rare condition detection.

One-Shot Learning Transfer Learning

Instant automatic diagnosis of diabetic retinopathy

no code implementations • 12 Jun 2019 • Gwenolé Quellec, Mathieu Lamard, Bruno Lay, Alexandre Le Guilcher, Ali Erginay, Béatrice Cochener, Pascale Massin

The purpose of this study is to evaluate the performance of the OphtAI system for the automatic detection of referable diabetic retinopathy (DR) and the automatic assessment of DR severity using color fundus photography.