HCS-TNAS: Hybrid Constraint-driven Semi-supervised Transformer-NAS for Ultrasound Image Segmentation
<|reference_start|>HCS-TNAS: Hybrid Constraint-driven Semi-supervised Transformer-NAS for Ultrasound Image Segmentation: Precise ultrasound segmentation is vital for clinicians to provide comprehensive diagnoses. However, developing a model that accurately segments ultrasound images is challenging due to the images' low quality and the scarcity of extensive labeled data. This results in two main solutions: (1) optimizing multi-scale feature representations, and (2) increasing resistance to data dependency. The first approach necessitates an advanced network architecture, but a handcrafted network is knowledge-intensive and often yields limited improvement. In contrast, neural architecture search (NAS) can more easily attain optimal performance, albeit with significant computational costs. Regarding the second issue, semi-supervised learning (SSL) is an established method, but combining it with complex NAS faces the risk of overfitting to a few labeled samples without extra constraints. Therefore, we introduce a hybrid constraint-driven semi-supervised Transformer-NAS (HCS-TNAS), balancing both solutions for segmentation. HCS-TNAS includes an Efficient NAS-ViT module for multi-scale token search before ViT's attention calculation, effectively capturing contextual and local information with lower computational costs, and a hybrid SSL framework that adds network independence and contrastive learning to the optimization for solving data dependency. By further developing a stage-wise optimization strategy, a rational network structure is identified. Experiments on public datasets show that HCS-TNAS achieves state-of-the-art performance, pushing the limit of ultrasound segmentation.<|reference_end|>
@article{chen2024hcs-tnas:,
    title={HCS-TNAS: Hybrid Constraint-driven Semi-supervised Transformer-NAS for
  Ultrasound Image Segmentation},
    author={Renqi Chen, Xinzhe Zheng, Haoyang Su, Kehan Wu},
    journal={arXiv preprint arXiv:2407.04203},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04203},
    primaryClass={cs.CV}
}