Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision

arXiv:2402.16008v1 Announce Type: cross
Abstract: The evolution of deep learning and artificial intelligence has significantly reshaped technological landscapes. However, their effective application in crucial sectors such as medicine demands more than just superior performance, but trustworthiness as well. While interpretability plays a pivotal role, existing explainable AI (XAI) approaches often do not reveal {\em Clever Hans} behavior where a model makes (ungeneralizable) correct predictions using spurious correlations or biases in data. Likewise, current post-hoc XAI methods are susceptible to generating …

abstract application artificial artificial intelligence arxiv cs.cv cs.lg deep learning dementia detection evolution intelligence interpretability masking medicine model interpretability performance pivotal precision role type