eXplainable Artificial Intelligence on Medical Images: A Survey

Over the last few years, the number of works about deep learning applied to the medical field has increased enormously. The necessity of a rigorous assessment of these models is required to explain these results to all people involved in medical exams. A recent field in the machine learning area is...

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Bibliographic Details
Published in:arXiv.org 2023-05
Main Authors: Matteus Vargas Simão da Silva, Rodrigo Reis Arrais, Jhessica Victoria Santos da Silva, Felipe Souza Tânios, Mateus, Antonio Chinelatto, Natalia Backhaus Pereira, De Paris, Renata, Ferreira Domingos, Lucas Cesar, Villaça, Rodrigo Dória, Vitor Lopes Fabris, Nayara Rossi Brito da Silva, Ana Claudia Akemi Matsuki de Faria, Jose Victor Nogueira Alves da Silva, Fabiana Cristina Queiroz de Oliveira Marucci, Francisco Alves de Souza Neto, Silva, Danilo Xavier, Kondo, Vitor Yukio, Claudio Filipi Gonçalves dos Santos
Format: Article
Language:English
Subjects:
Deep learning
Explainable artificial intelligence
Machine learning
Mathematical models
Medical imaging
Medical research
Physical examinations
Online Access:Get full text
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Summary:Over the last few years, the number of works about deep learning applied to the medical field has increased enormously. The necessity of a rigorous assessment of these models is required to explain these results to all people involved in medical exams. A recent field in the machine learning area is explainable artificial intelligence, also known as XAI, which targets to explain the results of such black box models to permit the desired assessment. This survey analyses several recent studies in the XAI field applied to medical diagnosis research, allowing some explainability of the machine learning results in several different diseases, such as cancers and COVID-19.
ISSN:2331-8422