Explainable Deep Learning Models in Medical Image Analysis

Amitojdeep Singh; Sourya Sengupta; Vasudevan Lakshminarayanan

doi:10.3390/jimaging6060052

Explainable Deep Learning Models in Medical Image Analysis

J Imaging. 2020 Jun 20;6(6):52. doi: 10.3390/jimaging6060052.

Authors

Amitojdeep Singh^{1

2}, Sourya Sengupta^{1

2}, Vasudevan Lakshminarayanan^{1

2}

Affiliations

¹ Theoretical and Experimental Epistemology Laboratory, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
² Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Abstract

Deep learning methods have been very effective for a variety of medical diagnostic tasks and have even outperformed human experts on some of those. However, the black-box nature of the algorithms has restricted their 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.

Keywords: XAI; deep learning; diagnosis; explainability; explainable AI; medical imaging.

Publication types

Review