Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation

Understand

Deep learning methods show promising results for overlapping cervical cell instance segmentation.

• However, in order to train a model with good generalization ability, voluminous pixel-level annotations are demanded which is quite expensive and time-consuming for acquisition.
• In this paper, we propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation.
• We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining (MMT-PSM), which consists of a teacher and a student network during training.