# Effective Pseudo-Labeling based on Heatmap for Unsupervised Domain Adaptation in Cell Detection

arXiv: https://arxiv.org/abs/2303.05269 · HTML: https://mlsanity.com/paper/2303.05269/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 55 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 30 of 32)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Strong-Weak Distribution Alignment for Adaptive Object Detection](https://mlsanity.com/paper/1812.04798/) (2018)
- [Multi-Adversarial Domain Adaptation](https://mlsanity.com/paper/1809.02176/) (2018)
- [Progressive Feature Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1811.08585/) (2018)
- [Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1607.03516/) (2016)
- [Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference](https://mlsanity.com/paper/1506.02158/) (2015)
- [Gradually Vanishing Bridge for Adversarial Domain Adaptation](https://mlsanity.com/paper/2003.13183/) (2020)
- [Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection](https://mlsanity.com/paper/1909.00597/) (2019)
- [Automatic adaptation of object detectors to new domains using self-training](https://mlsanity.com/paper/1904.07305/) (2019)
- [Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling](https://mlsanity.com/paper/1911.07982/) (2019)
- [Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1908.00262/) (2019)
- [Joint Geometrical and Statistical Alignment for Visual Domain Adaptation](https://mlsanity.com/paper/1705.05498/) (2017)
- [Curriculum Self-Paced Learning for Cross-Domain Object Detection](https://mlsanity.com/paper/1911.06849/) (2019)
- [Confidence Estimation via Auxiliary Models](https://mlsanity.com/paper/2012.06508/) (2020)
- [Unsupervised Hard Example Mining from Videos for Improved Object Detection](https://mlsanity.com/paper/1808.04285/) (2018)
- [Signet Ring Cell Detection With a Semi-supervised Learning Framework](https://mlsanity.com/paper/1907.03954/) (2019)
- [One-Shot Unsupervised Cross-Domain Detection](https://mlsanity.com/paper/2005.11610/) (2020)
- [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)
- [Semi-supervised Keypoint Localization](https://mlsanity.com/paper/2101.07988/) (2021)
- [Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition](https://mlsanity.com/paper/1903.10601/) (2019)

## Built on by (most cited first, 7 of 7)

- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing](https://mlsanity.com/paper/2304.10671/) (2023)
- [Towards Cross-Domain Continual Learning](https://mlsanity.com/paper/2402.12490/) (2024)
- [Toward unlabeled multi-view 3D pedestrian detection by generalizable AI: techniques and performance analysis](https://mlsanity.com/paper/2308.04515/) (2023)
- [CNG-SFDA:Clean-and-Noisy Region Guided Online-Offline Source-Free Domain Adaptation](https://mlsanity.com/paper/2401.14587/) (2024)

## Abstract (arXiv)

Cell detection is an important task in biomedical research. Recently, deep learning methods have made it possible to improve the performance of cell detection. However, a detection network trained with training data under a specific condition (source domain) may not work well on data under other conditions (target domains), which is called the domain shift problem. In particular, cells are cultured under different conditions depending on the purpose of the research. Characteristics, e.g., the shapes and density of the cells, change depending on the conditions, and such changes may cause domain shift problems. Here, we propose an unsupervised domain adaptation method for cell detection using a pseudo-cell-position heatmap, where the cell centroid is at the peak of a Gaussian distribution in the map and selective pseudo-labeling. In the prediction result for the target domain, even if the peak location is correct, the signal distribution around the peak often has a non-Gaussian shape. The pseudo-cell-position heatmap is thus re-generated using the peak positions in the predicted heatmap to have a clear Gaussian shape. Our method selects confident pseudo-cell-position heatmaps based on uncertainty and curriculum learning. We conducted numerous experiments showing that, compared with the existing methods, our method improved detection performance under different conditions.