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AI models for heart disease segmentation show poor generalization across datasets

By PulseAugur Editorial · [1 sources] ·

A new research paper on arXiv evaluates the out-of-distribution (OOD) generalization capabilities of deep learning models for segmenting congenital heart disease (CHD) from medical images. The study found that models performing well on in-distribution data often fail when applied to different datasets, highlighting a critical gap in current evaluation methods. Architectures like SwinUNETR demonstrated better robustness across varied imaging conditions compared to nnU-Net, especially when provided with limited target-domain supervision. AI

IMPACT Highlights the need for more robust evaluation metrics for medical imaging AI to ensure reliable clinical application.

RANK_REASON Research paper published on arXiv evaluating AI model generalization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI models for heart disease segmentation show poor generalization across datasets

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Research paper published on arXiv evaluating AI model generalization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Aniketh Vijesh, Shrisharanyan Vasu, Abhijit Ramesh, Clare Pomeroy-Ward, Harikrishnan Anil Maya, Sarin Xavier, Mahesh Kappanayil, Gilad Gressel ·

    Beyond In-Distribution Metrics: A Systematic Out-of-Distribution Evaluation of Congenital Heart Disease Segmentation

    arXiv:2609.17068v1 Announce Type: cross Abstract: Congenital heart disease (CHD) diagnosis and surgical planning often require patient-specific 3D anatomical models, but manual segmentation is labor-intensive, particularly in complex anatomies. Although deep-learning methods can …

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