Dual selective gleason pattern-aware multiple instance learning with uncertainty regularization for grade group prediction in histopathology images.

Although multiple instance learning (multiple instance learning) methods have advanced Gleason grading, most existing studies overlook the domain knowledge that Grade Group is determined by the joint contribution of different Gleason Patterns, thereby limiting both accuracy and interpretability.

In this study, we propose DSPA-U-multiple instance learning, an uncertainty-driven dual-selective Gleason Pattern-aware multiple instance learning model for patient-level Grade Group prediction.

In addition, we introduce an uncertainty constraint to mitigate the impact of noisy labels and enhance prediction robustness.