UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue Segmentation
<|reference_start|>UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue Segmentation: Ultrasound is widely used in clinical practice due to its affordability, portability, and safety. However, current AI research often overlooks combined disease prediction and tissue segmentation. We propose UniUSNet, a universal framework for ultrasound image classification and segmentation. This model handles various ultrasound types, anatomical positions, and input formats, excelling in both segmentation and classification tasks. Trained on a comprehensive dataset with over 9.7K annotations from 7 distinct anatomical positions, our model matches state-of-the-art performance and surpasses single-dataset and ablated models. Zero-shot and fine-tuning experiments show strong generalization and adaptability with minimal fine-tuning. We plan to expand our dataset and refine the prompting mechanism, with model weights and code available at (https://github.com/Zehui-Lin/UniUSNet).<|reference_end|>
@article{lin2024uniusnet:,
    title={UniUSNet: A Promptable Framework for Universal Ultrasound Disease
  Prediction and Tissue Segmentation},
    author={Zehui Lin, Zhuoneng Zhang, Xindi Hu, Zhifan Gao, Xin Yang, Yue Sun,
  Dong Ni and Tao Tan},
    journal={arXiv preprint arXiv:2406.01154},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01154},
    primaryClass={cs.CV}
}