Toward AI-Assisted Precision Diagnostics in Breast Cancer: Source-Group-Preserving Evaluation of Class-Imbalance Strategies for Ordered ER-IHC Segmentation

Vollständiger Abstract

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Background/Objectives: Estrogen receptor immunohistochemistry (ER-IHC) exhibits heterogeneous staining and substantial class imbalance, complicating segmentation of ordered expression categories. We evaluated whether class weighting, minority-focused crop sampling, adaptive minority curriculum (AMC), and Focal–Tversky optimization improved a common ResUNet-DS backbone for segmenting background/non-target pixels and C1 ER-negative, C2 weak-positive, C3 moderate-positive, and C4 strong-positive foreground categories. Methods: The dataset comprised 220 paired 512×512 image–mask patches organized into 44 recovered five-patch source groups. A source-group-preserving nested five-fold design used outer folds of 45, 45, 45, 45, and 40 patches. Six controlled training conditions were compared, with four independently selected inner models ensembled for each condition and outer fold. Results: Unweighted random training achieved the best numerical mean for all five primary endpoints: foreground Dice (0.7479±0.0123), C2–C4 Dice (0.7098±0.0128), foreground IoU (0.6074±0.0147), foreground quadratically weighted kappa (0.9761±0.0040), and foreground-ordinal MAE (0.0446±0.0062). Weighted random training was the strongest weighted/minority-sensitive condition but did not exceed the unweighted reference. None of 25 paired outer-fold comparisons reached p