C3MT:半监督医疗图像细分的自信校准对比平均师
Xianmin Wang1, Mingfeng Lin1, Jing Li2
1Institute of Artificial Intelligence, Guangzhou University, Guangzhou 511442, China.
概括
医疗图像细分的半监督学习通过新的自信校准对比平均教师 (C3MT) 框架得到了改进. C3MT提高了特征表示和细分质量,特别是在有限的标记数据下.
科学领域:
- 医学图像分析 医学图像分析
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 由于有限的标记数据,半监督学习对于医学图像细分至关重要.
- 现有的方法在特征表示,子网络分歧和杂的伪标签方面扎.
研究的目的:
- 引入一个新的框架,信心校准对比平均教师 (C3MT),以克服半监督医疗图像细分的局限性.
- 为了提高特征表示,训练稳定性和细分精度在低注释场景.
主要方法:
- 实施基于对比学习的联合培训策略,以适应性的不同意见调整为代表性多样性和培训稳定性.
- 引入了以信心校准和类别对准的不确定性为指导的区域混合策略,以过不可靠的伪标签并保持解剖学连贯性.
主要成果:
- C3MT显著提高了特征表示,培训稳定性和细分质量,特别是在低注释设置中.
- 在ACDC,Synapse和LA数据集上比最先进的方法取得了更高的性能.
- 在 20% 的标记数据中,在 ACDC 数据集上,平均子得分有 4.3% 的改善和 HD95 的> 1.0 mm 的减少.
结论:
- C3MT框架为半监督医疗图像细分提供了强大的解决方案,解决了现有方法的关键挑战.
- 提出的策略有效地提高了细分的准确性和可靠性,即使是最小的标记数据.
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