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• The proposed method achieves an F1-score of 0.8812 and an AUC of 0.9841 for PAM50 subtype classification on the internal TCGA-BRCA dataset.result 95%
• The method achieves an F1-score of 0.7952 and an AUC of 0.9512 on the external CPTAC-BRCA validation dataset.result 95%
• The method uses a novel optimization-driven framework combining NSGA-II with Monte Carlo dropout-based uncertainty estimation for patch selection.method 90%
• The method can identify a small but highly informative patch subset for classification, improving computational efficiency.technical 85%
• The framework reduces reliance on costly molecular assays by predicting PAM50 subtypes directly from H&E-stained whole-slide images.market 90%
• The method uses ResNet18 backbone for feature extraction and a custom CNN head for classification.technical 90%
• The proposed approach has the potential to support clinical decision-making as a scalable imaging-based replacement for molecular assays.market 80%
• The method outperforms existing methods in computational efficiency for histopathology-based PAM50 classification.technical 75%

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