# LG-NuSegHop: A Local-to-Global Self-Supervised Pipeline For Nuclei Instance Segmentation

arXiv: https://arxiv.org/abs/2511.04892 · HTML: https://mlsanity.com/paper/2511.04892/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 73 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Builds on (most cited first, 30 of 34)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification](https://mlsanity.com/paper/2108.11195/) (2021)
- [Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images](https://mlsanity.com/paper/2007.05448/) (2020)
- [CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation](https://mlsanity.com/paper/1903.05358/) (2019)
- [CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images](https://mlsanity.com/paper/2101.00442/) (2021)
- [Interpretable Convolutional Neural Networks via Feedforward Design](https://mlsanity.com/paper/1810.02786/) (2018)
- [Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2307.04189/) (2023)
- [Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting](https://mlsanity.com/paper/2005.02066/) (2020)
- [PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classification](https://mlsanity.com/paper/2002.03141/) (2020)
- [Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention](https://mlsanity.com/paper/2007.08373/) (2020)
- [Green Learning: Introduction, Examples and Outlook](https://mlsanity.com/paper/2210.00965/) (2022)
- [PseudoEdgeNet: Nuclei Segmentation only with Point Annotations](https://mlsanity.com/paper/1906.02924/) (2019)
- [Instance-aware Self-supervised Learning for Nuclei Segmentation](https://mlsanity.com/paper/2007.11186/) (2020)
- [On Supervised Feature Selection from High Dimensional Feature Spaces](https://mlsanity.com/paper/2203.11924/) (2022)
- [VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI](https://mlsanity.com/paper/2101.05131/) (2021)
- [Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training](https://mlsanity.com/paper/2202.08195/) (2022)
- [DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images](https://mlsanity.com/paper/2104.01325/) (2021)
- [Structure Embedded Nucleus Classification for Histopathology Images](https://mlsanity.com/paper/2302.11416/) (2023)
- [E-PixelHop: An Enhanced PixelHop Method for Object Classification](https://mlsanity.com/paper/2107.02966/) (2021)
- [BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels](https://mlsanity.com/paper/2401.07437/) (2024)
- [DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions](https://mlsanity.com/paper/2404.14956/) (2024)
- [Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI](https://mlsanity.com/paper/2107.10718/) (2021)
- [Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology Images](https://mlsanity.com/paper/2309.07394/) (2023)
- [Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation](https://mlsanity.com/paper/2306.02691/) (2023)
- [Dynamic Pseudo Label Optimization in Point-Supervised Nuclei Segmentation](https://mlsanity.com/paper/2406.16427/) (2024)
- [Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review](https://mlsanity.com/paper/2208.12460/) (2022)

## Abstract (arXiv)

Nuclei segmentation is the cornerstone task in histology image reading, shedding light on the underlying molecular patterns and leading to disease or cancer diagnosis. Yet, it is a laborious task that requires expertise from trained physicians. The large nuclei variability across different organ tissues and acquisition processes challenges the automation of this task. On the other hand, data annotations are expensive to obtain, and thus, Deep Learning (DL) models are challenged to generalize to unseen organs or different domains. This work proposes Local-to-Global NuSegHop (LG-NuSegHop), a self-supervised pipeline developed on prior knowledge of the problem and molecular biology. There are three distinct modules: (1) a set of local processing operations to generate a pseudolabel, (2) NuSegHop a novel data-driven feature extraction model and (3) a set of global operations to post-process the predictions of NuSegHop. Notably, even though the proposed pipeline uses { no manually annotated training data} or domain adaptation, it maintains a good generalization performance on other datasets. Experiments in three publicly available datasets show that our method outperforms other self-supervised and weakly supervised methods while having a competitive standing among fully supervised methods. Remarkably, every module within LG-NuSegHop is transparent and explainable to physicians.