Research Area:  Machine Learning

Abstract:

The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box-ness of deep learning models has raised the need for devising strategies to explain the decision process of these models, leading to the creation of the topic of eXplainable Artificial Intelligence (XAI). In this context, we provide a thorough survey of XAI applied to medical imaging diagnosis, including visual, textual, example-based and concept-based explanation methods. Moreover, this work reviews the existing medical imaging datasets and the existing metrics for evaluating the quality of the explanations. In addition, we include a performance comparison among a set of report generation-based methods. Finally, the major challenges in applying XAI to medical imaging and the future research directions on the topic are also discussed.

Keywords:  
Explainable Deep Learning
Medical Imaging Diagnosis
eXplainable Artificial Intelligence
Deep Learning
Machine Learning

Author(s) Name:   Cristiano Patrício, João C. Neves, Luís F. Teixeira

Publisher name:  arXiv:2205.04766

DOI:  10.48550/arXiv.2205.04766

Volume Information:  

Paper Link:   https://arxiv.org/abs/2205.04766