Deep Learning-Based Gleason Grading of Prostate Cancer detection from Histopathology Images

The Gleason grading of prostate cancer is well
known for being subjective in clinical practice, interpathologist
disagreement frequently going above 30% because of persistent
stromal noise and morphological heterogeneity, as well as the
ongoing difficulty in identifying rare high-risk patterns such as
the cribriform subtype. In this study, we introduce the Hybrid
Swin-MIL Network, an automated 5-class Gleason grading (NC,
GG3, GG4, GG5 and GG4-C) framework which has been
assessed on the SICAPv2 benchmark dataset in order to address
these diagnostic problems. We have incorporated into our
pipeline an annotation-guided masking approach that
successfully shuts off the non-target background stroma so that
feature learning is focused solely on the malignant epithelial
structures while still preserving the surrounding benign
morphology. A new architecture combines an intrapatch token
attention pooling hierarchical Swin Transformer with a parallel
multi-scale CNN branch in order to capture the overall
glandular structure while at the same time keeping the fine
cellular texture. Five separate models were trained using a
patient-stratified five-fold cross-validation method and these
were combined by soft voting during inference. We achieved
95.55% accuracy, together with a 95.58% macro F1-score and
95.81% recall for the aggressive GG4-Cribriform subtype
across (12,081) previously unseen test patches using our
ensemble. Indeed, these results highlight the advantages of using
both selective input masking and dual-branch attention, and
cross-validation ensembling provides a robust, reproducible
route to fine-grained prostate cancer grading.