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

arXiv: https://arxiv.org/abs/2007.10787 · HTML: https://mlsanity.com/paper/2007.10787/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 30 of 30)

- [Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2110.08762/) (2021)
- [Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation](https://mlsanity.com/paper/2112.09645/) (2021)
- [Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective](https://mlsanity.com/paper/2302.01735/) (2023)
- [Self-supervised driven consistency training for annotation efficient histopathology image analysis](https://mlsanity.com/paper/2102.03897/) (2021)
- [Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels](https://mlsanity.com/paper/2209.13476/) (2022)
- [Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis](https://mlsanity.com/paper/2208.08789/) (2022)
- [Revisiting Self-Distillation](https://mlsanity.com/paper/2206.08491/) (2022)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Deep Learning for Computational Cytology: A Survey](https://mlsanity.com/paper/2202.05126/) (2022)
- [InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation](https://mlsanity.com/paper/2206.15134/) (2022)
- [DoNet: Deep De-overlapping Network for Cytology Instance Segmentation](https://mlsanity.com/paper/2303.14373/) (2023)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays](https://mlsanity.com/paper/2104.03218/) (2021)
- [Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation](https://mlsanity.com/paper/2306.02691/) (2023)
- [Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation](https://mlsanity.com/paper/2402.04756/) (2024)
- [Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation](https://mlsanity.com/paper/2403.12767/) (2024)
- [The Four Color Theorem for Cell Instance Segmentation](https://mlsanity.com/paper/2506.09724/) (2025)
- [Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning](https://mlsanity.com/paper/2107.14476/) (2021)
- [Exploring Generalizable Distillation for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2207.12995/) (2022)
- [GAInS: Gradient Anomaly-aware Biomedical Instance Segmentation](https://mlsanity.com/paper/2409.13988/) (2024)
- [Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting](https://mlsanity.com/paper/2511.19953/) (2025)
- [Disco: Densely-overlapping Cell Instance Segmentation via Adjacency-aware Collaborative Coloring](https://mlsanity.com/paper/2602.05420/) (2026)
- [A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint](https://mlsanity.com/paper/2110.03535/) (2021)
- [DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network](https://mlsanity.com/paper/2203.06920/) (2022)
- [Exploring Contextual Relationships for Cervical Abnormal Cell Detection](https://mlsanity.com/paper/2207.04693/) (2022)
- [LightVessel: Exploring Lightweight Coronary Artery Vessel Segmentation via Similarity Knowledge Distillation](https://mlsanity.com/paper/2211.00899/) (2022)
- [One-shot and Partially-Supervised Cell Image Segmentation Using Small Visual Prompt](https://mlsanity.com/paper/2304.07991/) (2023)
- [Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation](https://mlsanity.com/paper/2311.08774/) (2023)
- [Skeleton-Guided Sparse Anchors for Rotated Instance Segmentation in Cell Microscopy](https://mlsanity.com/paper/2401.09895/) (2024)
- [INSITE: labelling medical images using submodular functions and semi-supervised data programming](https://mlsanity.com/paper/2402.07173/) (2024)

## Abstract (arXiv)

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. Two networks are encouraged to be consistent both in feature and semantic level under small perturbations. The teacher's self-ensemble predictions from K-time augmented samples are used to construct the reliable pseudo-labels for optimizing the student. We design a novel strategy to estimate the sensitivity to perturbations for each proposal and select informative samples from massive cases to facilitate fast and effective semantic distillation. In addition, to eliminate the unavoidable noise from the background region, we propose to use the predicted segmentation mask as guidance to enforce the feature distillation in the foreground region. Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only, and outperforms state-of-the-art semi-supervised methods.