具体来说,本文引入了体素稳定性约束(VSC)策略来优化参数,并在两个独立的初始化模型之间交换有效知识,从而突破性能瓶颈,避免模型崩溃。 此外,我们还提出了一种新的不确定性估计策略--体素可靠性约束(VRC),用于我们的半监督模型,以考虑局部区域层面的不确定性。我们进一步将模型扩展到辅助任务,并提出了带有不确定性估...
Pyramid Prediction Network for Semi-Supervised Segmentation Uncertainty Estimation based on Multi-Scale Discrepancy Uncertainty Rectifying Result Code Reference 本文提出了一种简单而高效的一致性正则化方法用于半监督医学图像分割,称为不确定性纠正金字塔一致性(URPC)。 Method 本文采用的网络结构如上图所示(Pyramid...
Semi-supervised medical image segmentation aims to leverage limited annotated data and rich unlabeled data to perform accurate segmentation. However, existing semi-supervised methods are highly dependent on the quality of self-generated pseudo labels, which are prone to incorrect supervision and ...
DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation Xinyu Liu,Wuyang Li,Yixuan Yuan The Chinese Univerisity of Hong Kong paper We propose DiffRect, a diffusion-based framework for semi-supervised medical image segmentation. It comprises two modules: the LCC aim...
作者希望在不同的目标、不同的模态和更广泛的分割任务上进行更全面的评估,并进一步扩大SemiSAM的灵活性,使其适应更多的半监督框架。 参考 [1].SemiSAM: Exploring SAM for Enhancing Semi-Supervised Medical Image Segmentation with Extremely Limited Annotations....
since the privacy policy and others. For easy evaluation and fair comparison, we are trying to build a semi-supervised medical image segmentation benchmark to boost the semi-supervised learning research in the medical image computing community.If you are interested, you can push your implementations...
The true label plays an important role in semi-supervised medical image segmentation (SSMIS) because it can provide the most accurate supervision information when the label is limited. The popular SSMIS method trains labeled and unlabeled data separately, and the unlabeled data cannot be directly sup...
Self-training with data augmentation emerges as an efficacious strategy for harnessing unlabeled data in the realm of semi-supervised medical image segmentation. Within the synthetic domain, existing models make a deliberate trade-off, sacrificing some of its absolute performance on labeled data to bols...
利用time-dependent Gaussian warming up function 来平衡监督损失和非监督损失。算法的整个流程如图所示: Result: code: https://github.com/HiLab-git/DTCgithub.com/HiLab-git/DTC 参考文献: [1] Semi-supervised Medical Image Segmentation through Dual-task Consistency...
Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation 1 Introduction 在本工作中,我们提出了一个全局的自监督对比和一个局部的全监督对比框架,以利用可用的标签。与文献中的无监督局部对比相比,有监督局部对比能更好地增强同一类内特征之间的相似性,并能区分不具有监督局部对比的特征。