تقرير
SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching
| العنوان: | SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching |
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| المؤلفون: | Zhao, Xingyue, Li, Peiqi, Luo, Xiangde, Yang, Meng, Chang, Shi, Li, Zhongyu |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: ISBI 2024 Oral |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.13553 |
| رقم الأكسشن: | edsarx.2407.13553 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |