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

• 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
• 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.

Related projects

• Rozsáhlé výpočetní systémy: modely, aplikace a verifikace VII.
• e-Infrastruktura CZ
• Aplikovaný výzkum v oblastech vyhledávání, analýz a vizualizací rozsáhlých dat, zpracování přirozeného jazyka a aplikované umělé inteligence
• Development of data processing techniques to support search, analysis and visualization of large datasets using artificial intelligence