Open ultrasound foundation model for robust segmentation and clinical measurement across heterogeneous settings

arXiv:2609.19230 · cs.CV · Submitted 2026-09-16 · Read on arXiv

cs.CV

Submitted: 2026-09-16

Updated: 2026-09-16

Terminology

Benchmark — 74× in this paper · explained in 2 episodes
Adaptation — 39× in this paper · explained in 1 episode
Accuracy — 33× in this paper · explained in 1 episode
Fine-Tuning — 29× in this paper · explained in 5 episodes
nnU-Net — 29× in this paper · explained in 2 episodes
Foundation Model — 21× in this paper · explained in 3 episodes
Generalization — 21× in this paper · explained in 13 episodes
Calibration — 11× in this paper · explained in 7 episodes
Robustness — 11× in this paper · explained in 5 episodes
Domain Shift — 9× in this paper · explained in 4 episodes
Distribution Shift — 8× in this paper · explained in 5 episodes
Ablation — 7× in this paper · explained in 1 episode
Transformer — 7× in this paper · explained in 2 episodes
Foundation Models — 5× in this paper · explained in 7 episodes
Iterative Refinement — 5× in this paper · explained in 3 episodes
Segmentation Masks — 5× in this paper · explained in 2 episodes
Verification — 5× in this paper · explained in 2 episodes
Instance Segmentation — 4× in this paper · explained in 3 episodes
Sample Efficiency — 3× in this paper · explained in 4 episodes
Transfer Learning — 3× in this paper · explained in 10 episodes
Adam — 2× in this paper · explained in 2 episodes
Core — 2× in this paper · explained in 2 episodes
Domain Adaptation — 2× in this paper · explained in 5 episodes
Downstream Tasks — 2× in this paper · explained in 2 episodes
European Conference on Computer Vision — 2× in this paper
Feature Extraction — 2× in this paper · explained in 2 episodes
International Conference on Learning Representations — 2× in this paper
Prompt Engineering — 2× in this paper · explained in 4 episodes
Provenance — 2× in this paper · explained in 3 episodes
Scaffolding — 2× in this paper · explained in 3 episodes
Uncertainty Quantification — 2× in this paper · explained in 8 episodes
APEX — 1× in this paper · explained in 1 episode
AUC — 1× in this paper · explained in 2 episodes
Ablation Studies — 1× in this paper · explained in 1 episode
Accountability — 1× in this paper · explained in 2 episodes
AdamW Optimizer — 1× in this paper · explained in 1 episode
Attention Mechanism — 1× in this paper · explained in 2 episodes
Benchmarking — 1× in this paper · explained in 3 episodes
CVF International Conference on Computer Vision — 1× in this paper
Consistency — 1× in this paper · explained in 3 episodes
Data Augmentation — 1× in this paper · explained in 7 episodes
Failure Modes — 1× in this paper · explained in 3 episodes
Frozen model — 1× in this paper · explained in 2 episodes
Generative AI — 1× in this paper · explained in 2 episodes
Generative Model — 1× in this paper · explained in 2 episodes
Generative Modeling — 1× in this paper · explained in 3 episodes
Guided attention — 1× in this paper · explained in 2 episodes
In-Context Learning — 1× in this paper · explained in 4 episodes
Loss Function — 1× in this paper · explained in 2 episodes
Medical Image Computing and Computer Assisted Intervention — 1× in this paper
Pairwise Comparisons — 1× in this paper · explained in 2 episodes
Semi-supervised Learning — 1× in this paper · explained in 4 episodes
Sheikh Tahnoon Bin Mohammed Medical City — 1× in this paper
Systematic Bias — 1× in this paper · explained in 3 episodes
Temporal consistency — 1× in this paper · explained in 2 episodes
Trace — 1× in this paper · explained in 2 episodes

Open ultrasound foundation model for robust segmentation and clinical measurement across heterogeneous settings

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