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arXiv · 2610.25202

ViCoR: Estimating Visual Necessity via Counterfactual Residuals for Multimodal Medical Data Selection

Haotian Gan·Yudong Li·Xintian Li·Jiarui Han·Weidong Tang
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Abstract

Medical vision-language models often exhibit hidden stratification, where high overall performance masks failures on subtle visual findings and overlapping clinical conditions. We trace this failure mode to modality imbalance during fine-tuning, where strong linguistic priors can suppress visually grounded learning and induce plausible but weakly grounded rationales. Existing data selection methods usually score image-text pairs as holistic units, without separating visually necessary samples from shortcut-solvable ones. To address this problem, we propose ViCoR, a counterfactual-residual-based data selection method for Med-VLM fine-tuning. ViCoR estimates visual necessity through image-dependent optimization residuals, calibrates the resulting scores with medical supervision compatibility, and constructs a diversity-aware fine-tuning subset. Across two backbones, four external generalization benchmarks, and one multi-disease robustness benchmark, ViCoR-selected 20\% subsets achieve the strongest average performance compared with full-data fine-tuning and selection baselines. Gradient-level analysis and perturbation audits further show that ViCoR enriches image-dependent training signals and strengthens image-level and evidence-region dependence. The code will be available at https://github.com/htgan-ai/ViCoR.

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Haotian Gan, Yudong Li, Xintian Li, Jiarui Han, Weidong Tang. 2026-10-18. ViCoR: Estimating Visual Necessity via Counterfactual Residuals for Multimodal Medical Data Selection. https://arxiv.org/abs/2610.25202

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