# Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency

arXiv: https://arxiv.org/abs/2107.08639 · HTML: https://mlsanity.com/paper/2107.08639/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 14 of 14)

- [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)
- [A Simple Semi-Supervised Learning Framework for Object Detection](https://mlsanity.com/paper/2005.04757/) (2020)
- [Proposal Learning for Semi-Supervised Object Detection](https://mlsanity.com/paper/2001.05086/) (2020)
- [Improving Landmark Localization with Semi-Supervised Learning](https://mlsanity.com/paper/1709.01591/) (2017)
- [Learning Temporal Pose Estimation from Sparsely-Labeled Videos](https://mlsanity.com/paper/1906.04016/) (2019)
- [Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection](https://mlsanity.com/paper/1803.09867/) (2018)
- [Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection](https://mlsanity.com/paper/2012.01950/) (2020)
- [Signet Ring Cell Detection With a Semi-supervised Learning Framework](https://mlsanity.com/paper/1907.03954/) (2019)
- [Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos](https://mlsanity.com/paper/1505.05769/) (2015)
- [Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response](https://mlsanity.com/paper/1911.13077/) (2019)
- [Semi-supervised Keypoint Localization](https://mlsanity.com/paper/2101.07988/) (2021)
- [Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria](https://mlsanity.com/paper/1807.00147/) (2018)
- [Deconvolving convolution neural network for cell detection](https://mlsanity.com/paper/1806.06970/) (2018)

## Built on by (most cited first, 1 of 1)

- [CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection](https://mlsanity.com/paper/2206.00338/) (2022)

## Abstract (arXiv)

Cell detection is the task of detecting the approximate positions of cell centroids from microscopy images. Recently, convolutional neural network-based approaches have achieved promising performance. However, these methods require a certain amount of annotation for each imaging condition. This annotation is a time-consuming and labor-intensive task. To overcome this problem, we propose a semi-supervised cell-detection method that effectively uses a time-lapse sequence with one labeled image and the other images unlabeled. First, we train a cell-detection network with a one-labeled image and estimate the unlabeled images with the trained network. We then select high-confidence positions from the estimations by tracking the detected cells from the labeled frame to those far from it. Next, we generate pseudo-labels from the tracking results and train the network by using pseudo-labels. We evaluated our method for seven conditions of public datasets, and we achieved the best results relative to other semi-supervised methods. Our code is available at https://github.com/naivete5656/SCDTC