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The paper presents the first systematic evaluation of out‑of‑distribution generalization for congenital heart disease (CHD) segmentation, using the ImageCHD cohort as a held‑out target. It compares several segmentation architectures under different training regimes, showing that in‑distribution performance is a poor predictor of cross‑cohort robustness: nnU‑Net drops from 0.77 to 0.51 Dice, while SwinUNETR maintains higher performance at 0.67 Dice. Limited target‑domain adaptation with only 11 labeled ImageCHD cases boosts all SwinUNETR variants above 0.76 Dice, highlighting the importance of explicit cross‑dataset testing.
The study benchmarks foundation models for chest X‑ray classification across a domain shift from adult to pediatric patients. Six foundation models and two CNN baselines were evaluated on a harmonized five‑label task using adult data for adaptation and tested on both adult and pediatric sets. Results show all models experience a substantial performance drop—over 10 percentage points—when applied to pediatric data, and deeper adaptation does not markedly improve cross‑population generalization.
AnyCXR is a unified framework that performs multi-organ segmentation on chest X-rays taken at any projection angle using only synthetic data. It employs a Multi-stage Domain Randomization engine to generate over 100,000 anatomically accurate synthetic radiographs from 3D CT volumes, and a Conditional Joint Annotation Regularization strategy to learn from partial, imperfect labels by enforcing anatomical consistency in a latent space. Trained solely on synthetic data, AnyCXR achieves strong zero-shot generalization on real-world datasets, accurately delineating 54 anatomical structures across PA, lateral, and oblique views, and supports downstream clinical tasks such as cardiothoracic ratio estimation, spine curvature assessment, and disease classification.
arXiv:2606. 16991v1 Announce Type: cross Abstract: Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition burden, and heavily contributes to radiologist workload.
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. Altho...
arXiv:2606. 19460v1 Announce Type: cross Abstract: We introduce the first generative foundation model for chest radiograph synthesis trained from scratch at the billion-parameter scale.