Which Anatomy Matters Under Limited Labels? A Data-Efficient Anatomy-Aware Benchmark for Cardiac Pathology Prediction

Researchers present an anatomy-aware benchmark demonstrating that in low-data medical imaging scenarios, effective representation of clinically meaningful cardiac structures outperforms model complexity for pathology prediction. The study uses cardiac MRI segmentation data to show that simpler classifiers with better anatomical feature engineering achieve superior results compared to more complex models with generic representations.

This research addresses a critical challenge in healthcare AI: the efficiency gap between resource-intensive deep learning systems and practical deployment in constrained medical settings. The study's core finding—that representation quality matters more than architectural complexity under limited labels—carries significant implications for how medical AI systems should be developed and deployed. By systematically comparing anatomy-specific features derived from cardiac structures against generic multi-structure representations across different classifier families, the researchers provide empirical evidence that domain knowledge integration substantially improves performance when training data is scarce. This contrasts with recent industry trends favoring increasingly complex neural architectures that require massive datasets. The ACDC benchmark approach, focusing on five-class cardiac pathology prediction, demonstrates measurable gains using straightforward linear and tree-based models augmented with clinically informed features. For healthcare organizations operating under budget and computational constraints—the majority globally—these findings suggest a more pragmatic path forward. Rather than investing heavily in cutting-edge hardware and large labeled datasets, institutions can achieve competitive diagnostic accuracy by prioritizing anatomical understanding and feature engineering. This shifts the optimization landscape toward domain expertise and thoughtful data representation rather than brute-force model scaling. The research indicates that successful medical AI adoption in resource-limited environments hinges on identifying which anatomical structures drive pathology predictions, then engineering representations that capture these relationships effectively. This principled approach aligns healthcare AI development with real-world deployment realities where computational and labeling resources remain constrained.

• →Feature representation quality surpasses model complexity as the primary driver of performance in low-data medical imaging scenarios.
• →Anatomy-specific representations from cardiac structures significantly outperform generic multi-structure approaches in pathology prediction tasks.
• →Linear and tree-based classifiers with domain-informed features achieve competitive results compared to more complex models, improving deployment feasibility.
• →Resource-constrained healthcare settings should prioritize clinical anatomy expertise and careful feature engineering over architectural complexity.
• →The findings suggest that medical AI success depends more on identifying informative anatomical structures than on increasing computational investment.