# Instance-aware Self-supervised Learning for Nuclei Segmentation

arXiv: https://arxiv.org/abs/2007.11186 · HTML: https://mlsanity.com/paper/2007.11186/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2020.

## Built on by (most cited first, 27 of 27)

- [Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts](https://mlsanity.com/paper/2109.04379/) (2021)
- [All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning](https://mlsanity.com/paper/2307.00290/) (2023)
- [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)
- [InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation](https://mlsanity.com/paper/2206.15134/) (2022)
- [Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training](https://mlsanity.com/paper/2202.08195/) (2022)
- [Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework](https://mlsanity.com/paper/2212.10305/) (2022)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)
- [Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions](https://mlsanity.com/paper/2205.09292/) (2022)
- [BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels](https://mlsanity.com/paper/2401.07437/) (2024)
- [SLPD: Slide-level Prototypical Distillation for WSIs](https://mlsanity.com/paper/2307.10696/) (2023)
- [Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review](https://mlsanity.com/paper/2208.12460/) (2022)
- [Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images](https://mlsanity.com/paper/2310.08501/) (2023)
- [CCRL: Contrastive Cell Representation Learning](https://mlsanity.com/paper/2208.06445/) (2022)
- [Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image Segmentation](https://mlsanity.com/paper/2208.10779/) (2022)
- [Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images](https://mlsanity.com/paper/2210.07862/) (2022)
- [Fine-grained Multi-class Nuclei Segmentation with Molecular-empowered All-in-SAM Model](https://mlsanity.com/paper/2508.15751/) (2025)
- [Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting](https://mlsanity.com/paper/2511.19953/) (2025)
- [Unsupervised Data-Driven Nuclei Segmentation For Histology Images](https://mlsanity.com/paper/2110.07147/) (2021)
- [HUNIS: High-Performance Unsupervised Nuclei Instance Segmentation](https://mlsanity.com/paper/2203.14887/) (2022)
- [Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification](https://mlsanity.com/paper/2203.16983/) (2022)
- [Consecutive Knowledge Meta-Adaptation Learning for Unsupervised Medical Diagnosis](https://mlsanity.com/paper/2209.10425/) (2022)
- [Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methods](https://mlsanity.com/paper/2308.03486/) (2023)
- [Pixel-global Self-supervised Learning with Uncertainty-aware Context Stabilizer](https://mlsanity.com/paper/2210.00646/) (2022)
- [GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis](https://mlsanity.com/paper/2301.04410/) (2023)
- [SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images](https://mlsanity.com/paper/2505.06710/) (2025)
- [LG-NuSegHop: A Local-to-Global Self-Supervised Pipeline For Nuclei Instance Segmentation](https://mlsanity.com/paper/2511.04892/) (2025)

## Abstract (arXiv)

Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology. The annotating of nuclei instances, requiring experienced pathologists to manually draw the contours, is extremely laborious and expensive, which often results in the deficiency of annotated data. The deep learning based segmentation approaches, which highly rely on the quantity of training data, are difficult to fully demonstrate their capacity in this area. In this paper, we propose a novel self-supervised learning framework to deeply exploit the capacity of widely-used convolutional neural networks (CNNs) on the nuclei instance segmentation task. The proposed approach involves two sub-tasks (i.e., scale-wise triplet learning and count ranking), which enable neural networks to implicitly leverage the prior-knowledge of nuclei size and quantity, and accordingly mine the instance-aware feature representations from the raw data. Experimental results on the publicly available MoNuSeg dataset show that the proposed self-supervised learning approach can remarkably boost the segmentation accuracy of nuclei instance---a new state-of-the-art average Aggregated Jaccard Index (AJI) of 70.63%, is achieved by our self-supervised ResUNet-101. To our best knowledge, this is the first work focusing on the self-supervised learning for instance segmentation.