Rad4XCNN: a new agnostic method for post-hoc global explanation of CNN-derived features by means of radiomics

arXiv:2405.02334v1 Announce Type: cross
Abstract: In the last years, artificial intelligence (AI) in clinical decision support systems (CDSS) played a key role in harnessing machine learning and deep learning architectures. Despite their promising capabilities, the lack of transparency and explainability of AI models poses significant challenges, particularly in medical contexts where reliability is a mandatory aspect. Achieving transparency without compromising predictive accuracy remains a key challenge. This paper presents a novel method, namely Rad4XCNN, to enhance the predictive power of …

abstract ai models architectures artificial artificial intelligence arxiv capabilities clinical cnn cs.ai cs.cv cs.lg decision decision support deep learning explainability features global intelligence key machine machine learning radiomics role support systems transparency type