Shedding light on the black box of a neural network used to detect prostate cancer in whole slide images by occlusion-based explainability

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Authors	GALLO Matej KRAJŇANSKÝ Vojtěch NENUTIL Rudolf HOLUB Petr BRÁZDIL Tomáš
Year of publication	2023
Type	Article in Periodical
Magazine / Source	NEW BIOTECHNOLOGY
MU Faculty or unit	Faculty of Informatics
Citation
Web	https://www.sciencedirect.com/science/article/pii/S1871678423000511
Doi	http://dx.doi.org/10.1016/j.nbt.2023.09.008
Keywords	Artificial intelligence; Digital histopathology; Explainable AI; Machine learning; Occlusion sensitivity analysis; Prostate cancer
Description	• Saliency maps identified histomorphological features characterizing cancer. • VGG16 model utilized all the structures that are observable by the pathologist. • The method can identify standard patterns not used by the model. • The method can also identify new patterns not yet used by human pathologists.
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