  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}.

Computer Vision and Pattern Recognition