eXplainable Artificial Intelligence on Medical Images: A Survey

• URL: http://arxiv.org/abs/2305.07511v1
• Date: Fri, 12 May 2023 14:25:42 GMT
• Title: eXplainable Artificial Intelligence on Medical Images: A Survey
• Authors: Matteus Vargas Sim\~ao da Silva, Rodrigo Reis Arrais, Jhessica Victoria Santos da Silva, Felipe Souza T\^anios, Mateus Antonio Chinelatto, Natalia Backhaus Pereira, Renata De Paris, Lucas Cesar Ferreira Domingos, Rodrigo D\'oria Villa\c{c}a, 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 Gon\c{c}alves dos Santos
• Abstract summary: 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. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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