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{this https URL}{GitHub}.

我们提出了一种使用两个不同子网络来探索和利用它们之间差异的方法，最终纠正错误的预测结果，通过有针对性的验证训练过程识别不一致预测的区域并进行微调，从而提高上下文信息的利用；此外，为了自适应地调整网络的表示能力并降低预测不确定性，我们采用了自监督对比学习范式，利用网络的置信度区分可靠和不可靠的预测，并训练模型有效地最小化不可靠的预测；我们的实验结果来自于临床 MRI 和 CT 扫描的器官分割，与最先进的方法相比，证明了我们方法的有效性。