  Incorporating pixel contextual information is critical for accurate
segmentation. In this paper, we show that an effective way to incorporate
contextual information is through a patch-based classifier. This patch
classifier is trained to identify classes present within an image region, which
facilitates the elimination of distractors and enhances the classification of
small object segments. Specifically, we introduce Multi-scale Patch-based
Multi-label Classifier (MPMC), a novel plug-in module designed for existing
semi-supervised segmentation (SSS) frameworks. MPMC offers patch-level
supervision, enabling the discrimination of pixel regions of different classes
within a patch. Furthermore, MPMC learns an adaptive pseudo-label weight, using
patch-level classification to alleviate the impact of the teacher's noisy
pseudo-label supervision the student. This lightweight module can be integrated
into any SSS framework, significantly enhancing their performance. We
demonstrate the efficacy of our proposed MPMC by integrating it into four SSS
methodologies and improving them across two natural image and one medical
segmentation dataset, notably improving the segmentation results of the
baselines across all the three datasets.
