Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges

arXiv:2403.20260v1 Announce Type: new
Abstract: Deep learning models have achieved high performance in medical applications, however, their adoption in clinical practice is hindered due to their black-box nature. Self-explainable models, like prototype-based models, can be especially beneficial as they are interpretable by design. However, if the learnt prototypes are of low quality then the prototype-based models are as good as black-box. Having high quality prototypes is a pre-requisite for a truly interpretable model. In this work, we propose a prototype …

abstract adoption analysis applications arxiv box cancer challenges clinical cs.cv deep learning design however low medical nature performance practice prediction prediction models type