Which Anatomy Matters Under Limited Labels? A Data-Efficient Anatomy-Aware Benchmark for Cardiac Pathology Prediction
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Electrical Engineering and Systems Science > Image and Video Processing
Title:Which Anatomy Matters Under Limited Labels? A Data-Efficient Anatomy-Aware Benchmark for Cardiac Pathology Prediction
Abstract:Numerous medical imaging problems must be solved under limited labels and constrained compute, yet it remains unclear whether performance gains are driven mainly by more expressive models or by better representation of clinically meaningful anatomy. We study this question through a low-data anatomy-aware benchmark for 5-class cardiac pathology prediction on the public ACDC MRI dataset. Using segmentation-derived patient descriptors from the right ventricle, myocardium, and left ventricle, we compare anatomy-specific and multi-structure representations across linear, kernel, and tree-based classifiers. We find that under limited label settings, representation dominates complexity. These results suggest that in resource-constrained healthcare settings, identifying and representing the most informative anatomy may matter more than the increasing complexity of the model alone.
| Comments: | ACCEPTED at ICML 2026 Workshop GlobalSouthML (Seoul, South Korea; PMLR 306, 2026) |
| Subjects: | Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Tissues and Organs (q-bio.TO) |
| Cite as: | arXiv:2606.06509 [eess.IV] |
| (or arXiv:2606.06509v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2606.06509
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