Explainable deep learning models in medical image analysis

TL;DR

This paper reviews the current state of explainable deep learning models in medical image analysis, emphasizing their importance for clinical deployment and discussing various approaches, challenges, and future research directions.

Contribution

It provides a comprehensive review of explainable deep learning applications in medical imaging, highlighting practical challenges and research gaps for clinical integration.

Findings

Various explainability approaches are used in medical imaging.

Challenges include clinical deployment and interpretability.

Further research is needed for practical clinical use.

Abstract

Deep learning methods have been very effective for a variety of medical diagnostic tasks and has even beaten human experts on some of those. However, the black-box nature of the algorithms has restricted clinical use. Recent explainability studies aim to show the features that influence the decision of a model the most. The majority of literature reviews of this area have focused on taxonomy, ethics, and the need for explanations. A review of the current applications of explainable deep learning for different medical imaging tasks is presented here. The various approaches, challenges for clinical deployment, and the areas requiring further research are discussed here from a practical standpoint of a deep learning researcher designing a system for the clinical end-users.