Type-Aware Representation Learning with Dual-Teacher Consistency for Label-Efficient Nuclei Segmentation

Nuclei instance segmentation in histopathology requires precise separation of touching nuclei and accurate cell-type classification. However, dense annotations are costly, and compact networks must preserve fine structural details while learning discriminative semantic representations. To address these challenges, we propose SPiN, a compact multitask architecture, together with dual-teacher Mean Teacher training (DTMT) for image-level semi-supervised nuclei instance segmentation. SPiN integrates a FastViT encoder, a parallel local enhancement pathway, and a shared decoder, with auxiliary type supervision to strengthen semantic representations. DTMT employs two exponential moving average teachers with distinct update rates to capture complementary temporal information. Their type predictions are combined through confidence-weighted fusion and filtered to provide soft supervision for unlabeled images, while geometric displacement regression remains supervised by labeled instances. Only the student network is required at inference. On PanNuke, the framework uses 12.50 million parameters and achieves bPQ/mPQ scores of 0.558/0.396 with 10% labeled training images, compared with 0.488/0.304 for supervised-only training and 0.553/0.390 for an adapted UniMatch baseline. These results demonstrate that integrating compact multitask modeling with type-focused temporal consistency improves annotation efficiency while maintaining a compact inference model, providing an effective approach to nuclei instance segmentation under limited supervision.