학술논문
eXplainable Artificial Intelligence on Medical Images: A Survey
Document Type
Working Paper
Author
da Silva, Matteus Vargas Simão; Arrais, Rodrigo Reis; da Silva, Jhessica Victoria Santos; Tânios, Felipe Souza; Chinelatto, Mateus Antonio; Pereira, Natalia Backhaus; De Paris, Renata; Domingos, Lucas Cesar Ferreira; Villaça, Rodrigo Dória; Fabris, Vitor Lopes; da Silva, Nayara Rossi Brito; de Faria, Ana Claudia Akemi Matsuki; da Silva, Jose Victor Nogueira Alves; Marucci, Fabiana Cristina Queiroz de Oliveira; Neto, Francisco Alves de Souza; Silva, Danilo Xavier; Kondo, Vitor Yukio; Santos, Claudio Filipi Gonçalves dos
Source
Subject
Language
Abstract
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.