Adaptive Deep Learning for Breast Cancer Subtype Prediction Via Misprediction Risk Analysis

ArXi:2503.12778v2 Announce Type: replace-cross Breast cancer remains a leading cause of cancer-related mortality worldwide. Early detection is critical, yet manual histopathology analysis is complex and subject to inter-observer variability. While deep neural network-based diagnostic systems have advanced binary tasks, they struggle with multiclass subtype prediction due to inter-class similarity, class imbalance, and domain shifts, resulting in frequent mispredictions.