# NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation

arXiv: https://arxiv.org/abs/2102.09099 · HTML: https://mlsanity.com/paper/2102.09099/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 90 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Simple Does It: Weakly Supervised Instance and Semantic Segmentation](https://mlsanity.com/paper/1603.07485/) (2016)
- [Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology](https://mlsanity.com/paper/1902.06543/) (2019)
- [PanNuke Dataset Extension, Insights and Baselines](https://mlsanity.com/paper/2003.10778/) (2020)
- [Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations](https://mlsanity.com/paper/2008.05571/) (2020)
- [Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge](https://mlsanity.com/paper/1807.08284/) (2018)
- [Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks](https://mlsanity.com/paper/1808.05896/) (2018)
- [Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images](https://mlsanity.com/paper/1810.00236/) (2018)
- [NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images](https://mlsanity.com/paper/2005.14511/) (2020)
- [Crowdsourcing in Computer Vision](https://mlsanity.com/paper/1611.02145/) (2016)
- [A Survey of Crowdsourcing in Medical Image Analysis](https://mlsanity.com/paper/1902.09159/) (2019)
- [Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types](https://mlsanity.com/paper/2002.07913/) (2020)
- [Teacher-Student chain for efficient semi-supervised histology image classification](https://mlsanity.com/paper/2003.08797/) (2020)
- [Designing Interpretable Approximations to Deep Reinforcement Learning](https://mlsanity.com/paper/2010.14785/) (2020)
- [A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study](https://mlsanity.com/paper/2010.06995/) (2020)
- [Fooling the Crowd with Deep Learning-based Methods](https://mlsanity.com/paper/1912.00142/) (2019)

## Built on by (most cited first, 30 of 34)

- [OCELOT: Overlapped Cell on Tissue Dataset for Histopathology](https://mlsanity.com/paper/2303.13110/) (2023)
- [Interactive Multi-Class Tiny-Object Detection](https://mlsanity.com/paper/2203.15266/) (2022)
- [A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model](https://mlsanity.com/paper/2507.17303/) (2025)
- [TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challenge](https://mlsanity.com/paper/2206.11943/) (2022)
- [Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning](https://mlsanity.com/paper/2306.00047/) (2023)
- [VOLTA: an Environment-Aware Contrastive Cell Representation Learning for Histopathology](https://mlsanity.com/paper/2303.04696/) (2023)
- [ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality](https://mlsanity.com/paper/2503.04522/) (2025)
- [Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review](https://mlsanity.com/paper/2208.12460/) (2022)
- [Variability Matters : Evaluating inter-rater variability in histopathology for robust cell detection](https://mlsanity.com/paper/2210.05175/) (2022)
- [Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits](https://mlsanity.com/paper/2112.09496/) (2021)
- [CCRL: Contrastive Cell Representation Learning](https://mlsanity.com/paper/2208.06445/) (2022)
- [MultiOrg: A Multi-rater Organoid-detection Dataset](https://mlsanity.com/paper/2410.14612/) (2024)
- [EM3M: An Electron Micrograph Dataset for Microstructural Segmentation and Generation](https://mlsanity.com/paper/2508.16239/) (2025)
- [A Multicentric Dataset for Training and Benchmarking Breast Cancer Segmentation in H&E Slides](https://mlsanity.com/paper/2510.02037/) (2025)
- [GrapHist: Graph Self-Supervised Learning for Histopathology](https://mlsanity.com/paper/2603.00143/) (2026)
- [NuNext: Reframing Nucleus Detection as Next-Point Detection](https://mlsanity.com/paper/2603.07098/) (2026)
- [Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations](https://mlsanity.com/paper/2302.08050/) (2023)
- [BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications](https://mlsanity.com/paper/2309.03509/) (2023)
- [UniCell: Universal Cell Nucleus Classification via Prompt Learning](https://mlsanity.com/paper/2402.12938/) (2024)
- [NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation](https://mlsanity.com/paper/2601.20104/) (2026)
- [Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy](https://mlsanity.com/paper/2606.12346/) (2026)
- [Nucleus Segmentation and Analysis in Breast Cancer with the MIScnn Framework](https://mlsanity.com/paper/2206.08182/) (2022)
- [Scale dependant layer for self-supervised nuclei encoding](https://mlsanity.com/paper/2207.10950/) (2022)
- [Unsupervised high-throughput segmentation of cells and cell nuclei in quantitative phase images](https://mlsanity.com/paper/2311.14639/) (2023)
- [Immunocto: a massive immune cell database auto-generated for histopathology](https://mlsanity.com/paper/2406.02618/) (2024)
- [Machine learning identification of maternal inflammatory response and histologic choroamnionitis from placental membrane whole slide images](https://mlsanity.com/paper/2411.02354/) (2024)
- [Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis](https://mlsanity.com/paper/2412.09521/) (2024)
- [Cancer cytoplasm segmentation in hyperspectral cell image with data augmentation](https://mlsanity.com/paper/2507.03325/) (2025)
- [Adaptive Multi-Scale Integration Unlocks Robust Cell Annotation in Histopathology Images](https://mlsanity.com/paper/2511.13586/) (2025)
- [Free Lunch in Medical Image Foundation Model Pre-training via Randomized Synthesis and Disentanglement](https://mlsanity.com/paper/2602.12317/) (2026)

## Abstract (arXiv)

High-resolution mapping of cells and tissue structures provides a foundation for developing interpretable machine-learning models for computational pathology. Deep learning algorithms can provide accurate mappings given large numbers of labeled instances for training and validation. Generating adequate volume of quality labels has emerged as a critical barrier in computational pathology given the time and effort required from pathologists. In this paper we describe an approach for engaging crowds of medical students and pathologists that was used to produce a dataset of over 220,000 annotations of cell nuclei in breast cancers. We show how suggested annotations generated by a weak algorithm can improve the accuracy of annotations generated by non-experts and can yield useful data for training segmentation algorithms without laborious manual tracing. We systematically examine interrater agreement and describe modifications to the MaskRCNN model to improve cell mapping. We also describe a technique we call Decision Tree Approximation of Learned Embeddings (DTALE) that leverages nucleus segmentations and morphologic features to improve the transparency of nucleus classification models. The annotation data produced in this study are freely available for algorithm development and benchmarking at: https://sites.google.com/view/nucls.