US-JEPA introduces a self‑supervised framework for ultrasound imaging that predicts masked latent representations instead of raw pixels, using a frozen, domain‑specific teacher to provide stable targets. This approach avoids the hyperparameter sensitivity and computational cost of traditional online teachers, enabling the student model to build upon the teacher’s semantic priors. The authors benchmark US‑JEPA against all publicly available ultrasound foundation models on UltraBench, showing competitive or superior performance across multiple organs and pathological conditions under linear probing.
By Ashwath Radhachandran, Vedrana Ivezi\'c, Shreeram Athreya, Corey W. Arnold, William Speier
The paper introduces Segment Anything Small (SAS), a data‑augmentation method that improves deep‑learning segmentation of small anatomical structures in ultrasound images. SAS uses two transformations: resizing and embedding organ thumbnails into a black background to vary organ scale, and adding noise to regions of interest to mimic tissue texture variability. Experiments on one internal and five external datasets show Dice score gains up to 0.35, with an average improvement of 0.16, and demonstrate that SAS enhances model robustness and generalizability without adding hallucinations or artifacts.
By Danielle L. Ferreira, Ahana Gangopadhyay, Hsi-Ming Chang, Ravi Soni, Gopal Avinash
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
arXiv:2608.29820v1 Announce Type: new
Abstract: Ultrasound B-mode imaging commonly suffers from speckle noise and artifacts, requiring a delicate balance between contrast, resolution, and preservatio...
By Juneyong Lee, Jaeyoung Choi
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.
By Weihao Gao
arXiv:2608. 14796v1 Announce Type: cross Abstract: Prostate cancer claims a life every 80 seconds.
By Ayusha Abbas, Saram Abbas, Kabita Adhikari
arXiv:2605. 12567v2 Announce Type: replace-cross Abstract: The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images.
By Jiajing Zhang, Bingze Dai, Xi Zhang, Yue Xu, Wei-Ning Lee
arXiv:2606. 25009v2 Announce Type: replace-cross Abstract: Ultrasound is a non-invasive, real-time, and cost-effective imaging technique widely used in clinical diagnosis.
By Yuexi Gu, Mengqi Wu, Yongheng Sun, Virginie Papadopoulou, Mingxia Liu, Maureen Kohi
arXiv:2608. 05471v1 Announce Type: cross Abstract: Prenatal ultrasound imaging is key for assessing fetal health, but AI progress is limited by scarce, privacy-restricted, and hard-to-annotate datasets.
By Harvey Mannering, Yilin Zhang, Ziao Liu, Zhiwu Huang, Jacqueline Matthew, Miguel Xochicale
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
arXiv:2605. 25402v2 Announce Type: replace-cross Abstract: Self-supervised pre-training paradigm has gained increasing prominence for learning transferable representations in medical imaging, yet existing methods for ultrasound (US) images operate at the image or frame level, overlooking the anatomical context for clinical-aligned representation learning.
By Chunzheng Zhu, Yijun Wang, Jianxin Lin, Feng Wang, Hongwei Wang, Lei Zhao, Shengli Li, Kenli Li
Ultrasound image classification is essential for computer-aided diagnosis. However, current methods often neglect clinical priors, leading to poor generalization in challenging scenarios and a lack of interpretability that limits clinical adoption.