半监督CT图像细分通过基于约束的对比学习进行半监督CT图像细分
Zhiyong Xiao1, Hao Sun1, Fei Liu2
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122 Jiangsu China.
Biomedical engineering letters
|September 2, 2024
概括
本研究引入了一种用于计算机断层扫描 (CT) 图像细分的新型半监督学习方法,通过受约束的对比学习有效利用未标记的数据来提高性能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 计算机断层扫描 (CT) 分段的深度学习方法需要大量的标记数据,这是昂贵的和耗时的.
- 现有的半监督方法与不可靠的伪标签作斗争,可能会降低模型性能.
研究的目的:
- 开发一种有效的半监督学习方法,用于CT图像分割,利用未标记的数据.
- 通过解决标记数据稀缺的局限性,提高医疗图像细分的准确性和效率.
主要方法:
- 一种新的半监督网络模型,集成卷积神经网络 (CNN) 和变压器,用于捕获本地和全球图像特征.
- 实施受约束的对比学习损失,以有效地利用未标记的数据,同时丢弃不可靠的样本.
- 在学生网络中包含剩余的挤压和激发模块,以增强特征表示.
主要成果:
- 与最先进的半监督技术相比,拟议的方法在COVID-19CT数据集上表现出卓越的性能.
- 在子相似系数 (DSC) 中实现了2.3%的改善,在雅卡德指数 (JC) 中实现了2.5%的改善.
- 将豪斯多夫距离第95百分点 (HD95) 减少了1.9毫米,表明边界精度有所改善.
结论:
- 开发的半监督方法通过将CNN和变压器架构与受约束的对比学习融合,有效地增强CT图像细分.
- 这种方法显著提高了未标记的医疗图像的利用率,为完全监督的方法提供了更有效的替代方案.
相关概念视频
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