# Single Image Estimation of Cell Migration Direction by Deep Circular Regression

arXiv: https://arxiv.org/abs/2406.19162 · HTML: https://mlsanity.com/paper/2406.19162/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Recent advances in directional statistics](https://mlsanity.com/paper/2005.06889/) (2020)

## Built on by (most cited first, 2 of 2)

- [Robustness of Generalized Median Computation for Consensus Learning in Arbitrary Spaces](https://mlsanity.com/paper/2503.05215/) (2025)
- [ANGLE: Angular Neural Generative Learning via Engression](https://mlsanity.com/paper/2607.12833/) (2026)

## Abstract (arXiv)

In this paper, we address the problem of estimating the migration direction of cells based on a single image. A solution to this problem lays the foundation for a variety of applications that were previously not possible. To our knowledge, there is only one related work that employs a classification CNN with four classes (quadrants). However, this approach does not allow for detailed directional resolution. We tackle the single image estimation problem using deep circular regression, with a particular focus on cycle-sensitive methods. On two common datasets, we achieve a mean estimation error of ~!17^circ, representing a significant improvement over previous work, which reported estimation error of 30^circ and 34^circ, respectively.