Which Anatomy Matters Under Limited Labels? A Data-Efficient Anatomy-Aware Benchmark for Cardiac Pathology Prediction
arXiv:2606.06509v1 Announce Type: cross 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 represen
The paper addresses the critical need to optimize AI model performance in medical imaging with limited data, a common constraint in real-world clinical settings, making it particularly relevant as AI adoption in healthcare accelerates.
This research provides a benchmark and methodology for developing data-efficient and clinically meaningful AI models for medical diagnosis, which can improve patient outcomes and resource allocation in healthcare.
The focus shifts towards understanding which anatomical representations are most effective for robust AI performance under data scarcity, guiding future model development and deployment in medical AI.
- · Medical AI developers
- · Healthcare providers
- · Patients with cardiac conditions
- · Medical imaging software companies
- · Developers of data-hungry, black-box AI models
- · Traditional diagnostic methods
Improved accuracy and efficiency of AI-driven cardiac pathology prediction, even with scarce training data.
Accelerated development and adoption of AI diagnostic tools in clinical practice due to lower data requirements and higher reliability.
Potential for more personalized and preventative cardiac care, driven by widely accessible and accurate AI diagnostics.
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Read at arXiv cs.LG