ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging

arXiv:2404.04736v1 Announce Type: cross
Abstract: The adoption of Deep Learning algorithms in the medical imaging field is a prominent area of research, with high potential for advancing AI-based Computer-aided diagnosis (AI-CAD) solutions. However, current solutions face challenges due to a lack of interpretability features and high data demands, prompting recent efforts to address these issues. In this study, we propose the ProtoAL method, where we integrate an interpretable DL model into the Deep Active Learning (DAL) framework. This approach aims …

abstract active learning adoption algorithms arxiv cad challenges computer cs.ai cs.cv cs.lg current data data demands deep learning deep learning algorithms diagnosis face features however imaging interpretability medical medical imaging prompting research solutions type