Robust, credible, and interpretable AI-based histopathological prostate cancer grading.
Fabian WesthaeusserPatrick FuhlertEsther DietrichMaximilian LennartzRobin KhatriNico KaiserPontus RöbeckRoman David BülowSaskia von StillfriedAnja WitteSam LadjevardiAnders DrottePeter SevergardhJan BaumbachVictor G PuellesMichael HäggmanMichael BrehlerPeter BoorPeter WalhagenAnca DragomirChrister BuschMarkus GraefenEwert BengtssonGuido SauterMarina ZimmermannStefan BonnPublished in: medRxiv : the preprint server for health sciences (2024)
Data variation poses serious risks for AI-based histopathological PCa grading, even when models are trained on large datasets. Algorithmic improvements for model robustness, interpretability, credibility, and training on high-variance data as well as outcome-based severity prediction gives rise to robust models with above ISUP-level PCa grading performance.