Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
This work addresses the need for improved diagnostic tools in prostate cancer, but it is incremental as it compares existing architectures without introducing new methods.
This study evaluated 11 deep neural network architectures for automated Gleason grading in prostate cancer, finding that ConvNeXt performed best on a dataset of 34,264 annotated tissue tiles, though challenges remained in distinguishing closely related grades.
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.