# UltraStar: Semantic-Aware Star Graph Modeling for Echocardiography Navigation

arXiv: https://arxiv.org/abs/2603.01461 · HTML: https://mlsanity.com/paper/2603.01461/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 26 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs](https://mlsanity.com/paper/2303.00915/) (2023)
- [USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient Image Analysis](https://mlsanity.com/paper/2401.00153/) (2023)
- [LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching](https://mlsanity.com/paper/2306.11925/) (2023)
- [Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound](https://mlsanity.com/paper/2007.04480/) (2020)
- [CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning](https://mlsanity.com/paper/2504.13820/) (2025)
- [Cardiac Copilot: Automatic Probe Guidance for Echocardiography with World Model](https://mlsanity.com/paper/2406.13165/) (2024)
- [EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance](https://mlsanity.com/paper/2504.13065/) (2025)
- [Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train](https://mlsanity.com/paper/2406.19756/) (2024)
- [UltraSeP: Sequence-aware Pre-training for Echocardiography Probe Movement Guidance](https://mlsanity.com/paper/2408.15026/) (2024)
- [UltraHiT: A Hierarchical Transformer Architecture for Generalizable Internal Carotid Artery Robotic Ultrasonography](https://mlsanity.com/paper/2509.13832/) (2025)
- [UltraDP: Generalizable Carotid Ultrasound Scanning with Force-Aware Diffusion Policy](https://mlsanity.com/paper/2511.15550/) (2025)
- [VA-Adapter: Adapting Ultrasound Foundation Model to Echocardiography Probe Guidance](https://mlsanity.com/paper/2510.06809/) (2025)

## Abstract (arXiv)

Echocardiography is critical for diagnosing cardiovascular diseases, yet the shortage of skilled sonographers hinders timely patient care, due to high operational difficulties. Consequently, research on automated probe navigation has significant clinical potential. To achieve robust navigation, it is essential to leverage historical scanning information, mimicking how experts rely on past feedback to adjust subsequent maneuvers. Practical scanning data collected from sonographers typically consists of noisy trajectories inherently generated through trial-and-error exploration. However, existing methods typically model this history as a sequential chain, forcing models to overfit these noisy paths, leading to performance degradation on long sequences. In this paper, we propose UltraStar, which reformulates probe navigation from path regression to anchor-based global localization. By establishing a Star Graph, UltraStar treats historical keyframes as spatial anchors connected directly to the current view, explicitly modeling geometric constraints for precise positioning. We further enhance the Star Graph with a semantic-aware sampling strategy that actively selects the representative landmarks from massive history logs, reducing redundancy for accurate anchoring. Extensive experiments on a dataset with over 1.31 million samples demonstrate that UltraStar outperforms baselines and scales better with longer input lengths, revealing a more effective topology for history modeling under noisy exploration. Code is available at https://github.com/LeapLabTHU/UltraStar.