Are Explanations Helpful? A Comparative Analysis of Explainability Methods in Skin Lesion Classifiers

Autor:	Paccotacya-Yanque, Rosa Y. G., Bissoto, Alceu, Avila, Sandra
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Deep Learning has shown outstanding results in computer vision tasks; healthcare is no exception. However, there is no straightforward way to expose the decision-making process of DL models. Good accuracy is not enough for skin cancer predictions. Understanding the model's behavior is crucial for clinical application and reliable outcomes. In this work, we identify desiderata for explanations in skin-lesion models. We analyzed seven methods, four based on pixel-attribution (Grad-CAM, Score-CAM, LIME, SHAP) and three on high-level concepts (ACE, ICE, CME), for a deep neural network trained on the International Skin Imaging Collaboration Archive. Our findings indicate that while these techniques reveal biases, there is room for improving the comprehensiveness of explanations to achieve transparency in skin-lesion models. Comment: 6 pages. Paper accepted at 20th International Symposium on Medical Information Processing and Analysis (SIPAIM)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.03166 Zobrazit plný text záznamu View this record from Arxiv