bibtext: @InProceedings{ Gao_Aligning_MICCAI2024,
                   author = { Gao, Yunhe and Gu, Difei and Zhou, Mu and Metaxas, Dimitris },
                   title = { { Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
abstract: Although explainability is essential in the clinical diagnosis, most deep learning models still function as black boxes without elucidating their decision-making process. In this study, we investigate the explainable model development that can mimic the decision-making process of human experts by fusing the domain knowledge of explicit diagnostic criteria. We introduce a simple yet effective framework, Explicd, towards Explainable language-informed criteria-based diagnosis. Explicd initiates its process by querying domain knowledge from either large language models (LLMs) or human experts to establish diagnostic criteria across various concept axes (e.g., color, shape, texture, or specific patterns of diseases). By leveraging a pretrained vision-language model, Explicd injects these criteria into the embedding space as knowledge anchors, thereby facilitating the learning of corresponding visual concepts within medical images. The final diagnostic outcome is determined based on the similarity scores between the encoded visual concepts and the textual criteria embeddings. Through extensive evaluation on five medical image classification benchmarks, Explicd has demonstrates its inherent explianability and extends to improve classification performance compared to traditional black-box models.
title: Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification
authors: Gao, Yunhe Gu, Difei Zhou, Mu Metaxas, Dimitris