Self-supervised learning (SSL) can reduce the need for labelled medical images, but the choice of pretext objective remains unclear for lung ultrasound (LUS). Contrastive learning, masked reconstructi...
arXiv:2609.16551v1 Announce Type: new Abstract: Self-supervised learning (SSL) can reduce the need for labelled medical images, but the choice of pretext objective remains unclear for lung ultrasound...
arXiv:2609.16775v1 Announce Type: new Abstract: Annotating large radiology datasets is bottlenecked by the manual effort of delineating structures slice-by-slice in 3D volumes. Interactive methods re...
arXiv:2610.03290v1 Announce Type: cross Abstract: Lung ultrasound (LUS) is attractive for tuberculosis (TB) screening at primary-care level, but labelled cohorts are small. Echocardiography carries n...
arXiv:2607. 22824v1 Announce Type: cross Abstract: Comparing CT reconstruction methods fairly is labor-intensive and largely manual, and many benchmarks use idealized data.
arXiv:2608. 14796v1 Announce Type: cross Abstract: Prostate cancer claims a life every 80 seconds.
arXiv:2608.28715v1 Announce Type: cross Abstract: Ultrasound-guided interventions can require localization of an untracked 2D frame within a 3D anatomical reference. Rigid slice-to-volume registratio...
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.
The paper introduces SPAR‑Bench, a set of eight probes designed to test whether medical vision models can reason about anatomy in abdominal CT scans. Experiments across five architectures and three foundation models—both frozen and fine‑tuned—show that while models can recall canonical organ locations, they fail to perform relational reasoning or spatial comparisons within a patient, even under zero‑shot transfer. The study also demonstrates that pooled probing underestimates a model’s relational capabilities and that open‑weight multimodal large language models perform poorly on these tasks.
arXiv:2608. 12086v1 Announce Type: cross Abstract: Vision-language models, such as contrastive language-image pre-training (CLIP)-based approaches, have reached state-of-the-art (SOTA) results in medical artificial intelligence.
The paper introduces CAMEO, a framework for generating ultrasound reports from multiple images that emphasizes clinical awareness and evidence grounding. It consists of three stages: learning visual-language primitives, cross-view evidence grounding using distilled image-report pairs, and clinically aware preference alignment with error-oriented pairs. Experiments on the USReport-Distilled benchmark show significant improvements over prior models in BLEU, ROUGE, METEOR, and ClinicalScore metrics.
arXiv:2608. 28063v1 Announce Type: new Abstract: Multi-organ ultrasound classifiers increasingly combine attention, mixture-of-experts routing, uncertainty gating, and evidential deep learning (EDL) objectives to address heterogeneous anatomy and acquisition.