Explainable Deep Learning Methods in Medical Image Classification: A Survey

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Patrício, C (Author) Other; Neves, C (Author) Other; Luis F Teixeira (Author) FEUP

Journal

Title: ACM Computing Surveys

Vol. 56

Initial page: 85:1

ISSN: 0360-0300

Publisher: ACM

Indexing

ISI Web of Knowledge - 15 Citations

Scopus - 23 Citations

Other information

Authenticus ID: P-00Z-38K

DOI: 10.1145/3625287

Abstract (EN): 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 nature 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 discussed.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 41