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 +13 more
5/12/2023
cs.LGcs.AIcs.CY

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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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Cite this paper

@article{silva2023explainable,
  title  = {eXplainable Artificial Intelligence on Medical Images: A Survey},
  author = {Matteus Vargas Simão da Silva and Rodrigo Reis Arrais and Jhessica Victoria Santos da Silva and Felipe Souza Tânios and Mateus Antonio Chinelatto and Natalia Backhaus Pereira and Renata De Paris and Lucas Cesar Ferreira Domingos and Rodrigo Dória Villaça and Vitor Lopes Fabris and Nayara Rossi Brito da Silva and Ana Claudia Akemi Matsuki de Faria and Jose Victor Nogueira Alves da Silva and Fabiana Cristina Queiroz de Oliveira Marucci and Francisco Alves de Souza Neto and Danilo Xavier Silva and Vitor Yukio Kondo and Claudio Filipi Gonçalves dos Santos},
  year   = {2023},
  eprint = {2305.07511},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.07511v1}
}

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