eXplainable Artificial Intelligence on Medical Images: A Survey
Matteus Vargas Simão da Silva Rodrigo Reis Arrais Jhessica Victoria Santos da Silva Felipe Souza Tânios Mateus Antonio Chinelatto Natalia Backhaus Pereira Renata De Paris Lucas Cesar Ferreira Domingos Rodrigo Dória Villaça 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 Danilo Xavier Silva Vitor Yukio Kondo Claudio Filipi Gonçalves dos Santos
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.