SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching
<|reference_start|>SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching: Automated nodule segmentation is essential for computer-assisted diagnosis in ultrasound images. Nevertheless, most existing methods depend on precise pixel-level annotations by medical professionals, a process that is both costly and labor-intensive. Recently, segmentation foundation models like SAM have shown impressive generalizability on natural images, suggesting their potential as pseudo-labelers. However, accurate prompts remain crucial for their success in medical images. In this work, we devise a novel weakly supervised framework that effectively utilizes the segmentation foundation model to generate pseudo-labels from aspect ration annotations for automatic nodule segmentation. Specifically, we develop three types of bounding box prompts based on scalable shape priors, followed by an adaptive pseudo-label selection module to fully exploit the prediction capabilities of the foundation model for nodules. We also present a SAM-driven uncertainty-aware cross-teaching strategy. This approach integrates SAM-based uncertainty estimation and label-space perturbations into cross-teaching to mitigate the impact of pseudo-label inaccuracies on model training. Extensive experiments on two clinically collected ultrasound datasets demonstrate the superior performance of our proposed method.<|reference_end|>
@article{zhao2024sam-driven,
    title={SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware
  Cross Teaching},
    author={Xingyue Zhao, Peiqi Li, Xiangde Luo, Meng Yang, Shi Chang, Zhongyu Li},
    journal={arXiv preprint arXiv:2407.13553},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13553},
    primaryClass={cs.CV}
}