XAIMed: A Diagnostic Support Tool for Explaining AI Decisions on Medical Images

Mattia Daole, Pietro Ducange, Francesco Marcelloni, Giustino Claudio Miglionico, Alessandro Renda, Alessio Schiavo, Alessio Schiavo

2024

Abstract

Convolutional Neural Networks have demonstrated high accuracy in medical image analysis, but the opaque nature of such deep learning models hinders their widespread acceptance and clinical adoption. To address this issue, we present XAIMed, a diagnostic support tool specifically designed to be easy to use for physicians. XAIMed supports diagnostic processes involving the analysis of medical images through Convolutional Neural Networks. Besides the model prediction, XAIMed also provides visual explanations using four state-of-art eXplainable AI methods: LIME, RISE, Grad-CAM, and Grad-CAM++. These methods produce saliency maps which highlight image regions that are most influential for a model decision. We also introduce a simple strategy for aggregating the different saliency maps into a unified view which reveals a coarse-grained level of agreement among the explanations. The application features an intuitive graphical user interface and is designed in a modular fashion thus facilitating the integration of new tasks, new models, and new explanation methods.

Paper Citation

in Harvard Style

Daole M., Ducange P., Marcelloni F., Claudio Miglionico G., Renda A. and Schiavo A. (2024). XAIMed: A Diagnostic Support Tool for Explaining AI Decisions on Medical Images. In Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS; ISBN 978-989-758-720-7, SciTePress, pages 27-37. DOI: 10.5220/0012942000003886

in Bibtex Style

@conference{explains24,
author={Mattia Daole and Pietro Ducange and Francesco Marcelloni and Giustino Claudio Miglionico and Alessandro Renda and Alessio Schiavo},
title={XAIMed: A Diagnostic Support Tool for Explaining AI Decisions on Medical Images},
booktitle={Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS},
year={2024},
pages={27-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012942000003886},
isbn={978-989-758-720-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS
TI - XAIMed: A Diagnostic Support Tool for Explaining AI Decisions on Medical Images
SN - 978-989-758-720-7
AU - Daole M.
AU - Ducange P.
AU - Marcelloni F.
AU - Claudio Miglionico G.
AU - Renda A.
AU - Schiavo A.
PY - 2024
SP - 27
EP - 37
DO - 10.5220/0012942000003886
PB - SciTePress