bibtext: @InProceedings{ Pla_ARegionBased_MICCAI2024,
                   author = { Playout, Clément and Legault, Zacharie and Duval, Renaud and Boucher, Marie Carole and Cheriet, Farida },
                   title = { { A Region-Based Approach to Diabetic Retinopathy Classification with Superpixel Tokenization } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: We explore the efficacy of a region-based method for image tokenization, aimed at enhancing the resolution of images fed to a Transformer. This method involves segmenting the image into regions using SLIC superpixels. Spatial features, derived from a pretrained model are aggregated segment-wise and input into a streamlined Vision Transformer (ViT). Our model introduces two novel contributions: the matching of segments to semantic prototypes and the graph-based clustering of tokens to merge similar adjacent segments. This approach leads to a model that not only competes effectively in classifying diabetic retinopathy but also produces high-resolution attribution maps, thereby enhancing the interpretability of its predictions.
title: A Region-Based Approach to Diabetic Retinopathy Classification with Superpixel Tokenization
authors: Playout, Clément Legault, Zacharie Duval, Renaud Boucher, Marie Carole Cheriet, Farida