ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging
This provides a more interpretable and data-efficient solution for AI-based computer-aided diagnosis in medical imaging, addressing trust and scarcity issues for domain experts.
The study tackled the challenges of interpretability and high data demands in deep learning for medical imaging by proposing ProtoAL, an interpretable deep active learning method using prototypes, which achieved an area under the precision-recall curve of 0.79 on the Messidor dataset while using only 76.54% of labeled data.
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 to address both challenges by focusing on the medical imaging context and utilizing an inherently interpretable model based on prototypes. We evaluated ProtoAL on the Messidor dataset, achieving an area under the precision-recall curve of 0.79 while utilizing only 76.54\% of the available labeled data. These capabilities can enhances the practical usability of a DL model in the medical field, providing a means of trust calibration in domain experts and a suitable solution for learning in the data scarcity context often found.