Explainable Deep Learning Methods in Medical Image Classification: A Survey

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024

Autores

Patrício, C (Autor) Outra; Neves, C (Autor) Outra; Luis F Teixeira (Autor) FEUP

Revista

Título: ACM Computing Surveys

Vol. 56

Página Inicial: 85:1

ISSN: 0360-0300

Editora: ACM

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 2 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 41