In plain words: This survey reviews ways to show why deep-learning systems flag diseases like cancer and COVID-19 in medical scans, instead of leaving them as black boxes. It finds these tools can shed light on results across several diseases, helping doctors judge how a diagnosis was reached.

Abstract · 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 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.

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, et al.
arXiv:2305.07511 · cs.LG, cs.AI, cs.CY, eess.IV · submitted May 12, 2023
abstract · pdf · html