Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer

arXiv:2403.16695v1 Announce Type: cross
Abstract: 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 …

abstract advanced architectures artificial artificial intelligence arxiv automated cancer cs.cv cs.lg deep learning deep neural network diagnostic digital digital pathology eess.iv health intelligence network neural network pathology performance q-bio.to study tools type