# Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework

arXiv: https://arxiv.org/abs/2212.10305 · HTML: https://mlsanity.com/paper/2212.10305/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 63 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 30 of 35)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [SinGAN: Learning a Generative Model from a Single Natural Image](https://mlsanity.com/paper/1905.01164/) (2019)
- [Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss](https://mlsanity.com/paper/1708.00961/) (2017)
- [Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation](https://mlsanity.com/paper/1706.04737/) (2017)
- [Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network](https://mlsanity.com/paper/1806.05473/) (2018)
- [Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations](https://mlsanity.com/paper/1911.01218/) (2019)
- [Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks](https://mlsanity.com/paper/1908.02498/) (2019)
- [Micro-Net: A unified model for segmentation of various objects in microscopy images](https://mlsanity.com/paper/1804.08145/) (2018)
- [Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images](https://mlsanity.com/paper/1810.00236/) (2018)
- [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)
- [Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2007.09854/) (2020)
- [Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting](https://mlsanity.com/paper/2005.02066/) (2020)
- [Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2007.07695/) (2020)
- [Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention](https://mlsanity.com/paper/2007.08373/) (2020)
- [Instance-aware Self-supervised Learning for Nuclei Segmentation](https://mlsanity.com/paper/2007.11186/) (2020)
- [SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation](https://mlsanity.com/paper/2106.01544/) (2021)
- [DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images](https://mlsanity.com/paper/2104.01325/) (2021)
- [Boundary-assisted Region Proposal Networks for Nucleus Segmentation](https://mlsanity.com/paper/2006.02695/) (2020)

## Built on by (most cited first, 14 of 14)

- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [Diffusion-based Data Augmentation for Nuclei Image Segmentation](https://mlsanity.com/paper/2310.14197/) (2023)
- [Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection](https://mlsanity.com/paper/2310.14154/) (2023)
- [Cell Graph Transformer for Nuclei Classification](https://mlsanity.com/paper/2402.12946/) (2024)
- [Generating Progressive Images from Pathological Transitions via Diffusion Model](https://mlsanity.com/paper/2311.12316/) (2023)
- [Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation](https://mlsanity.com/paper/2402.16280/) (2024)
- [Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images](https://mlsanity.com/paper/2310.14226/) (2023)
- [Prompt-based Grouping Transformer for Nucleus Detection and Classification](https://mlsanity.com/paper/2310.14176/) (2023)
- [UniCell: Universal Cell Nucleus Classification via Prompt Learning](https://mlsanity.com/paper/2402.12938/) (2024)
- [Microscopy Image Segmentation via Point and Shape Regularized Data Synthesis](https://mlsanity.com/paper/2308.09835/) (2023)
- [OCU-Net: A Novel U-Net Architecture for Enhanced Oral Cancer Segmentation](https://mlsanity.com/paper/2310.02486/) (2023)
- [Instance Migration Diffusion for Nuclear Instance Segmentation in Pathology](https://mlsanity.com/paper/2504.01577/) (2025)
- [ConvNeXt-FD: A Fractal-Based Deep Model for Robust Biomedical Image Segmentation](https://mlsanity.com/paper/2605.22002/) (2026)
- [End-to-End Cell Detection via Instance-aware Graph Modeling](https://mlsanity.com/paper/2609.15354/) (2026)

## Abstract (arXiv)

Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H&E stained pathology images. However, it is inefficient and unnecessary to label all pixels for a dataset of nuclei images which usually contain similar and redundant patterns. Although unsupervised and semi-supervised learning methods have been studied for nuclei segmentation, very few works have delved into the selective labeling of samples to reduce the workload of annotation. Thus, in this paper, we propose a novel full nuclei segmentation framework that chooses only a few image patches to be annotated, augments the training set from the selected samples, and achieves nuclei segmentation in a semi-supervised manner. In the proposed framework, we first develop a novel consistency-based patch selection method to determine which image patches are the most beneficial to the training. Then we introduce a conditional single-image GAN with a component-wise discriminator, to synthesize more training samples. Lastly, our proposed framework trains an existing segmentation model with the above augmented samples. The experimental results show that our proposed method could obtain the same-level performance as a fully-supervised baseline by annotating less than 5% pixels on some benchmarks.