Dual-Stage Deep Learning Framework for Prostate Cancer Grading Using Swin U-Net and Attention-Based CNNs

Abstract

Accurate grading of prostatic adenocarcinoma is essential in treatment?planning. However, Gleason grading is time-consuming and clinically undependable. We presented a hybrid?deep learning framework which comprises Swin U-Net for transformer-based segmentation network and attention-based CNNs for ISUP grade classification task. We incorporated Grad-CAM to aid?in model interpretability and to visualize decision crucial areas. Quantitative evaluations on the PANDA, ISUP Grade-wise and transverse datasets achieve 100% accuracy on the smaller balanced?Transverse dataset, 90.2 ± 0.7% performance in terms of ISUP with only 3.5M parameters, and a vicious Dice score equal to 0.99 ± 0.005 for segmentation. Notably, this cross-dataset generalization has not deteriorated below 92.3 ± 1.4% in any TIO experiment with no form?of retraining applied to the transferred models. Inference time is less than?20 ms, deployment on the edge and mobile. The proposed model has achieved state-of-the-art performance for interpretability,?accuracy, and computational complexity. The broadcast-then-categorize platform has been validated in ablation?and optimization experiments, which demonstrate the potential for real-time diagnosis of prostate cancer.