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.
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.
UltraPIPS introduces domain‑specific foundation models for measuring perceptual similarity in B‑mode ultrasound images. The study shows that ultrasound‑trained LPIPS backbones better correlate with downstream tasks such as classification, segmentation, and reconstruction than natural‑image or general medical models. Optimizing LPIPS loss with an ultrasound backbone yields a strong balance between reconstruction quality and realism, and the authors provide an open‑source library for these metrics.
arXiv:2608. 13939v1 Announce Type: cross Abstract: Ultrasound is the primary imaging modality for assessing thyroid nodules, and the ACR TI-RADS framework standardizes diagnosis through five ultrasound feature categories that are aggregated into five risk levels (TR1-TR5).
The paper introduces ExiL, a mask‑conditioned progressive learning framework for bone ultrasound segmentation that models annotation as a structured refinement trajectory. ExiL uses a synthetic expert‑like brush simulator and a lightweight U‑Net to learn from imperfect masks, and it can be updated in real time from expert refinements. In experiments on UltraBones100k and a prospective volunteer dataset, ExiL cut average annotation time from 60 to 20 seconds per frame and improved mean Dice by about 0.045, achieving 0.87 Dice and 2.7 px boundary error with 10–50 ms inference.
arXiv:2607. 00744v1 Announce Type: cross Abstract: Prenatal anomaly classification and localization is of critical importance for fetal health and pregnancy management.
arXiv:2606. 29586v1 Announce Type: cross Abstract: Vision-language foundation models have shown strong potential in medical image analysis.
arXiv:2607. 14703v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become the main paradigm for whole-slide image (WSI) analysis in computational pathology.
arXiv:2604. 19191v2 Announce Type: replace-cross Abstract: Deploying AI-based anomaly detection across diverse clinical imaging settings remains challenging because most existing methods rely on modality-specific architectures, anatomical priors, or extensive retraining, limiting their use as general-purpose screening tools.
arXiv:2605. 23995v4 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabeled data.
arXiv:2608. 19863v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has driven substantial progress in audio representation learning, though existing methods have increasingly relied on elaborate pre-training recipes to reach competitive performance.
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.
The paper introduces Dual Co-Train, a source‑free domain adaptation framework for ultrasound tongue segmentation that operates under extreme data scarcity. Starting from a lightweight UltraUNet pretrained on only five labeled images, the method iteratively refines pseudo‑labels, filters unreliable masks with a contour‑based quality‑control module, and generates target‑style synthetic image‑mask pairs via a segmentation‑guided conditional GAN. The student model is trained on a mix of clean pseudo‑labeled target images, noisy pseudo‑labels with consistency regularization, and synthetic samples, enabling closed‑loop adaptation without access to source data. Experiments on 12 source‑target transfer pairs across eight datasets show that this approach improves segmentation overlap and contour accuracy over both unsupervised baselines and supervised models.