arXiv:2608. 04766v1 Announce Type: cross Abstract: A large number of infants with congenital anomalies are born each year globally, especially in areas with underdeveloped medical resources.
arXiv:2607. 18283v1 Announce Type: cross Abstract: Accurate localization of the corpus callosum (CC) in fetal ultrasound (US) images is crucial for the early identification of neurodevelopmental abnormalities.
arXiv:2606. 11106v1 Announce Type: cross Abstract: A global shortage of trained sonographers limits prenatal ultrasound screening in low- and middle-income countries, where over half of pregnant women receive no skilled sonography.
arXiv:2609.15225v1 Announce Type: new Abstract: Objective: To develop an intelligent framework, termed CUA-Net, for the automated classification of congenital uterine anomalies (CUA) without requirin...
arXiv:2607. 00744v1 Announce Type: cross Abstract: Prenatal anomaly classification and localization is of critical importance for fetal health and pregnancy management.
The paper introduces SonoCorpus, an open dataset of 456,963 ultrasound images with 1,626,085 expert masks from 53 public sources across 24 clinical applications and 17 countries, and SonoBase, an interactive segmentation foundation model pretrained on this data. SonoBase outperforms existing models (SAM2, MedSAM2, MedSAM3) on fifteen diverse evaluation datasets, matching specialist models and achieving clinically relevant accuracy for metrics such as ejection fraction, fetal head circumference, and gestational age. The authors provide full reproducibility resources, including checkpoints, optimizer states, and starter code, to enable community adoption and further development.
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...
AnatoProto is a lightweight sequence‑level framework that adapts a frozen BiomedCLIP encoder for detecting the fetal abdominal circumference standard plane in low‑cost obstetric blind sweeps. It incorporates anatomy‑weighted spatial pooling, a within‑case prototype loss, a three‑stage cascade refinement, and a hybrid prediction head to address the highly imbalanced, short‑segment nature of the task. On the ACOUSLIC‑AI benchmark, AnatoProto achieves a test F1 of 67.72, surpassing the best foundation‑model baseline by 13.20 F1 and the best video temporal‑action‑detection baseline by 15.76 F1.
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.
arXiv:2510. 12953v4 Announce Type: replace-cross Abstract: Recent medical vision-language models have shown promise on tasks such as VQA, report generation, and anomaly detection.
arXiv:2606. 19174v1 Announce Type: cross Abstract: Clinician-centered evaluation is critical for validating medical AI systems, especially in ultrasound imaging where quantitative metrics do not always capture clinical usability.
arXiv:2608. 14763v1 Announce Type: cross Abstract: Assessment of ventriculomegaly (VM) on fetal brain ultrasound relies primarily on measuring lateral ventricular atrial width on standard planes, which is operator-dependent and may not fully reflect the overall ventricular enlargement.
This study evaluates the classification accuracy of six modern deep‑learning architectures—VGG19, ResNet50, GoogleNet, ConvNeXt, EfficientNet, and Vision Transformers—on breast ultrasound images categorized by BI‑RADS. Using 2,945 training images and 936 validation images from 1,540 patients, the models were tested in full fine‑tuning, linear evaluation, and training‑from‑scratch settings. The best performance was achieved with full fine‑tuning, yielding 76.39 % accuracy and a 67.94 % F1 score.