通过自监督对比学习利用无标签数据进行3D医学图像分割
Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning
AI总结：
针对3D半监督分割中上下文利用有限和伪标签不可靠的问题，提出双子网络差异验证训练与自监督对比学习，有效提升临床MRI和CT器官分割性能。
摘要：
当前的3D半监督分割方法面临重大挑战，例如对上下文信息的考虑有限，以及无法生成可靠的伪标签以有效利用无监督数据。为了应对这些挑战，我们引入了两个不同的子网络，旨在探索和利用它们之间的差异，最终纠正错误的预测结果。更具体地说，我们识别预测不一致的区域，并启动有针对性的验证训练过程。该过程策略性地微调并协调子网络的预测，从而增强对上下文信息的利用。此外，为了自适应地微调网络的表示能力并减少预测不确定性，我们采用自监督对比学习范式。为此，我们利用网络的置信度来区分可靠和不可靠的预测。随后对模型进行训练，以有效减少不可靠的预测。我们从临床MRI和CT扫描中获得的器官分割实验结果证明，与最先进的方法相比，我们的方法是有效的。代码库可在GitHub上获取。
英文摘要：
Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-labels for effective unsupervised data use. To address these challenges, we introduce two distinct subnetworks designed to explore and exploit the discrepancies between them, ultimately correcting the erroneous prediction results. More specifically, we identify regions of inconsistent predictions and initiate a targeted verification training process. This procedure strategically fine-tunes and harmonizes the predictions of the subnetworks, leading to enhanced utilization of contextual information. Furthermore, to adaptively fine-tune the network's representational capacity and reduce prediction uncertainty, we employ a self-supervised contrastive learning paradigm. For this, we use the network's confidence to distinguish between reliable and unreliable predictions. The model is then trained to effectively minimize unreliable predictions. Our experimental results for organ segmentation, obtained from clinical MRI and CT scans, demonstrate the effectiveness of our approach when compared to state-of-the-art methods. The codebase is accessible on \href{https://github.com/xmindflow/SSL-contrastive}{GitHub}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
