Causally Guided Chromosomal Abnormality Detection for Unseen Configurations

Chromosomal structural abnormality detection informs genetic diagnosis and counseling. However, scarce abnormal samples cannot fully cover naturally occurring configurations across rearrangement locations and intervals, while patient backgrounds and preparation and imaging conditions alter their appearance. Detection thus requires generalization to unseen configurations and robustness to environmental shifts. We observe progressive degradation under these shifts and near-chance performance on unseen configurations. Our causally guided two-stage framework combines homologous comparison with normal-statistics calibration, using abundant normal pairs as a baseline to recast normal-versus-abnormal classification as distinguishing normal from abnormal variation. This reduces reliance on chromosome-specific appearance and simplifies learning from scarce abnormal samples. Stage I combines skeleton-guided counterfactual generation, configuration generalization learning, and synthetic bias suppression to expand configuration coverage and learn transferable structural evidence. Stage II freezes the encoder and calibration statistics, refining the classification head through quality and reference constraints for environmental robustness. We introduce ChromoShift, a full-pipeline dataset comprising skeleton-centromere annotations, normal pretraining data, and real abnormality data under distribution shifts. Experiments show gains in both test domains, achieving 90.83% AUROC on unseen configurations. The framework supports clinical assessment of known abnormalities and screening for potentially novel configurations to guide further genetic research. Code: https://anonymous.4open.science/r/CausalChrom-3D66/.