Assessing the Performance of Deep Learning for Automated Gleason Grading
  in Prostate Cancer
Dominik Müller Philip Meyer Lukas Rentschler Robin Manz Daniel Hieber Jonas Bäcker Samantha Cramer Christoph Wengenmayr Bruno Märkl Ralf Huss Frank Kramer Iñaki Soto-Rey Johannes Raffler
Prostate cancer is a dominant health concern calling for advanced diagnostic
tools. Utilizing digital pathology and artificial intelligence, this study
explores the potential of 11 deep neural network architectures for automated
Gleason grading in prostate carcinoma focusing on comparing traditional and
recent architectures. A standardized image classification pipeline, based on
the AUCMEDI framework, facilitated robust evaluation using an in-house dataset
consisting of 34,264 annotated tissue tiles. The results indicated varying
sensitivity across architectures, with ConvNeXt demonstrating the strongest
performance. Notably, newer architectures achieved superior performance, even
though with challenges in differentiating closely related Gleason grades. The
ConvNeXt model was capable of learning a balance between complexity and
generalizability. Overall, this study lays the groundwork for enhanced Gleason
grading systems, potentially improving diagnostic efficiency for prostate
cancer.