# Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations

arXiv: https://arxiv.org/abs/2106.15918 · HTML: https://mlsanity.com/paper/2106.15918/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Positive-Unlabeled Learning with Non-Negative Risk Estimator](https://mlsanity.com/paper/1703.00593/) (2017)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Signet Ring Cell Detection With a Semi-supervised Learning Framework](https://mlsanity.com/paper/1907.03954/) (2019)
- [Resolving Class Imbalance in Object Detection with Weighted Cross Entropy Losses](https://mlsanity.com/paper/2006.01413/) (2020)
- [Object Detection as a Positive-Unlabeled Problem](https://mlsanity.com/paper/2002.04672/) (2020)

## Built on by (most cited first, 9 of 9)

- [SATr: Slice Attention with Transformer for Universal Lesion Detection](https://mlsanity.com/paper/2203.07373/) (2022)
- [Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation](https://mlsanity.com/paper/2304.07519/) (2023)
- [Few-Shot Domain Adaptive Object Detection for Microscopic Images](https://mlsanity.com/paper/2407.07633/) (2024)
- [Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations](https://mlsanity.com/paper/2302.08050/) (2023)
- [Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training](https://mlsanity.com/paper/2309.13306/) (2023)
- [Adaptive Pseudo Label Selection for Individual Unlabeled Data by Positive and Unlabeled Learning](https://mlsanity.com/paper/2508.07548/) (2025)
- [LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screening](https://mlsanity.com/paper/2306.03407/) (2023)
- [XAI-guided Insulator Anomaly Detection for Imbalanced Datasets](https://mlsanity.com/paper/2409.16821/) (2024)
- [Sequence models for by-trial decoding of cognitive strategies from neural data](https://mlsanity.com/paper/2504.10028/) (2025)

## Abstract (arXiv)

Cell detection in histopathology images is of great value in clinical practice. textitConvolutional neural networks (CNNs) have been applied to cell detection to improve the detection accuracy, where cell annotations are required for network training. However, due to the variety and large number of cells, complete annotations that include every cell of interest in the training images can be challenging. Usually, incomplete annotations can be achieved, where positive labeling results are carefully examined to ensure their reliability but there can be other positive instances, i.e., cells of interest, that are not included in the annotations. This annotation strategy leads to a lack of knowledge about true negative samples. Most existing methods simply treat instances that are not labeled as positive as truly negative during network training, which can adversely affect the network performance. In this work, to address the problem of incomplete annotations, we formulate the training of detection networks as a positive-unlabeled learning problem. Specifically, the classification loss in network training is revised to take into account incomplete annotations, where the terms corresponding to negative samples are approximated with the true positive samples and the other samples of which the labels are unknown. To evaluate the proposed method, experiments were performed on a publicly available dataset for mitosis detection in breast cancer cells, and the experimental results show that our method improves the performance of cell detection given incomplete annotations for training.