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.
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:2606. 15176v1 Announce Type: cross Abstract: Ultrasound imaging is the most widely adopted medical modality globally due to its low cost and portability, yet artificial intelligence (AI) deployment remains constrained by reliance on GPU-accelerated models, creating a structural paradox where the cost of "intelligence" exceeds that of the imaging device itself.
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.
Risk‑Routed Implicit Boundary Refinement (RIBR) is a compact framework designed to improve boundary delineation in medical ultrasound images. It refines convolutional segmentation by applying spatially weighted implicit residuals, where a coordinate‑conditioned implicit branch predicts logit corrections and a learned gate modulates their influence based on predictive uncertainty and local appearance. Geometry‑ and speckle‑aware regularization provides contour supervision, and RIBR outperforms ten baselines across nine datasets, achieving a macro‑average Dice of 76.67% and the lowest Hausdorff distance on all datasets.
The study investigates how few expert-annotated cases are needed to fine‑tune MedSAM3 for abdominal organ segmentation using Low‑Rank Adaptation (LoRA). With only 10 annotated CT or MRI cases, the LoRA‑adapted models achieve performance comparable to specialist systems that require orders of magnitude more data, including reliable gallbladder segmentation and near‑state‑of‑the‑art results for liver, kidneys, and spleen. The approach also generalizes to cardiac segmentation on the Whole Heart dataset, and training takes only 3–5 hours per organ on a single GPU, roughly twice as fast as nnU-Net.
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.
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: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:2608. 14796v1 Announce Type: cross Abstract: Prostate cancer claims a life every 80 seconds.
arXiv:2608. 10903v1 Announce Type: cross Abstract: Reliable clinical deployment of machine learning requires models that know when they are likely to fail, particularly for subgroups underrepresented in training data.
arXiv:2605. 02942v2 Announce Type: replace Abstract: Fairness studies of medical imaging AI often explain subgroup performance gaps through under-representation in the training data.
FETALFusion is a dual‑path CNN‑Mamba encoder‑decoder designed for fetal head segmentation in 2D ultrasound across transabdominal and transperineal acquisition types. It uses four‑directional state‑space scans at the highest‑resolution stages, domain‑specific channel gates, and a weak landmark shape prior, achieving a Dice score of 0.963 and an HD95 of 2.10 mm on a held‑out test set of 471 images. Ablation studies show that the landmark prior and domain gates drive most of the performance gains, while the state‑space branch has minimal impact. whyItMatters":"The method provides highly accurate fetal head segmentation without test‑time augmentation or domain labels, enabling reliable head circumference measurement for monitoring fetal growth."