Subject-disjoint evaluation on C-NMC 2019 reduces AUROC by about 0.04 versus random splits, with EfficientNet-B1 reaching 0.913 AUROC, 0.87 sensitivity, and 0.80 specificity under honest conditions with calibration assessment.
I., Alalwan, N., & Masood, A
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A Leakage-Aware Comparative Benchmark of Machine Learning, Deep Learning, and Transformer Models for Reliable Leukemia Detection
Subject-disjoint evaluation on C-NMC 2019 reduces AUROC by about 0.04 versus random splits, with EfficientNet-B1 reaching 0.913 AUROC, 0.87 sensitivity, and 0.80 specificity under honest conditions with calibration assessment.