# Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification

arXiv: https://arxiv.org/abs/2107.09289 · HTML: https://mlsanity.com/paper/2107.09289/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Positive-Unlabeled Learning with Non-Negative Risk Estimator](https://mlsanity.com/paper/1703.00593/) (2017)
- [Class-prior Estimation for Learning from Positive and Unlabeled Data](https://mlsanity.com/paper/1611.01586/) (2016)
- [Learning Temporal Pose Estimation from Sparsely-Labeled Videos](https://mlsanity.com/paper/1906.04016/) (2019)
- [Signet Ring Cell Detection With a Semi-supervised Learning Framework](https://mlsanity.com/paper/1907.03954/) (2019)
- [Object Detection as a Positive-Unlabeled Problem](https://mlsanity.com/paper/2002.04672/) (2020)
- [Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos](https://mlsanity.com/paper/1505.05769/) (2015)
- [Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes](https://mlsanity.com/paper/1907.09140/) (2019)
- [Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response](https://mlsanity.com/paper/1911.13077/) (2019)

## Built on by (most cited first, 3 of 3)

- [CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection](https://mlsanity.com/paper/2206.00338/) (2022)
- [Adaptive Pseudo Label Selection for Individual Unlabeled Data by Positive and Unlabeled Learning](https://mlsanity.com/paper/2508.07548/) (2025)
- [Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping](https://mlsanity.com/paper/2307.04113/) (2023)

## Abstract (arXiv)

Cell detection is an essential task in cell image analysis. Recent deep learning-based detection methods have achieved very promising results. In general, these methods require exhaustively annotating the cells in an entire image. If some of the cells are not annotated (imperfect annotation), the detection performance significantly degrades due to noisy labels. This often occurs in real collaborations with biologists and even in public data-sets. Our proposed method takes a pseudo labeling approach for cell detection from imperfect annotated data. A detection convolutional neural network (CNN) trained using such missing labeled data often produces over-detection. We treat partially labeled cells as positive samples and the detected positions except for the labeled cell as unlabeled samples. Then we select reliable pseudo labels from unlabeled data using recent machine learning techniques; positive-and-unlabeled (PU) learning and P-classification. Experiments using microscopy images for five different conditions demonstrate the effectiveness of the proposed method.