arXiv AI June 16, 2026 By Weihao Gao

Enabling Real-Time Point-of-Care Ultrasound Segmentation: A GPU-Free Deployment in Resource-Limited Settings

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

computer-vision

arXiv AI

Jun 10

FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model

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.

By Mahmood Alzubaidi, Uzair Shah, Raden Muaz, Ines Abbes, Nader Mohammed, Abdullatif Magram, Khalid Alyafei, Mowafa Househ, Marco Agus

llms agents computer-vision fine-tuning efficiency multimodal safety

arXiv AI

Aug 6

FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening

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.

By Bin Pu, Jiewen Yang, Liwen Wang, Ying Tan, Guannan He, Xingbo Dong, Qika Lin, Jiarong Guo, Lixian Yang, Zuozhu Liu, Shengli Li, Kenli Li

computer-vision fine-tuning benchmarks

arXiv AI

Jun 18

A Clinician-Centered Pipeline for Annotation and Evaluation in Ultrasound AI Studies

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.

By Fangyijie Wang, Jianjun Yu, Wentao Shi, Haixia Huang, Ran Shi, Gu\'enol\'e Silvestre, Kathleen M. Curran

computer-vision

arXiv AI

Jul 22

FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images

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.

By Alessandro Di Matteo, Sara Moccia, Giuseppe Rizzo, Gianpaolo Grisolia, Ricciarda Raffaelli, Lorenzo Vasciaveo, Francesco D'Antonio, Maria Chiara Fiorentino

computer-vision fine-tuning safety

arXiv Machine Learning

1d ago

Zero-Shot Adaptation of Medical Vision Foundation Models for High-Frequency Micro-Ultrasound Prostate Segmentation

arXiv:2608. 14796v1 Announce Type: cross Abstract: Prostate cancer claims a life every 80 seconds.

By Ayusha Abbas, Saram Abbas, Kabita Adhikari

computer-vision

arXiv AI

Aug 5

Externally Validated Breast Ultrasound Segmentation via Multi-task Learning with BI-RADS-Consistent Morphological Priors

arXiv:2511. 15968v2 Announce Type: replace-cross Abstract: External validation of breast ultrasound segmentation models remains limited because internal train--test splits do not capture domain shifts across imaging systems, acquisition protocols, and patient populations.

By Jingru Zhang, Saed Moradi, Ashirbani Saha

computer-vision benchmarks

Related stories

FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model

FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening

A Clinician-Centered Pipeline for Annotation and Evaluation in Ultrasound AI Studies

FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images

Zero-Shot Adaptation of Medical Vision Foundation Models for High-Frequency Micro-Ultrasound Prostate Segmentation

Externally Validated Breast Ultrasound Segmentation via Multi-task Learning with BI-RADS-Consistent Morphological Priors