# CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection

arXiv: https://arxiv.org/abs/2206.00338 · HTML: https://mlsanity.com/paper/2206.00338/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 1 Jun 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [Attention-Based Transformers for Instance Segmentation of Cells in Microstructures](https://mlsanity.com/paper/2011.09763/) (2020)
- [CircleNet: Anchor-free Detection with Circle Representation](https://mlsanity.com/paper/2006.02474/) (2020)
- [Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification](https://mlsanity.com/paper/2107.09289/) (2021)
- [EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring](https://mlsanity.com/paper/2204.03014/) (2022)
- [Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency](https://mlsanity.com/paper/2107.08639/) (2021)

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

- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
- [Self-supervised pseudo-colorizing of masked cells](https://mlsanity.com/paper/2302.05968/) (2023)
- [A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention](https://mlsanity.com/paper/2407.18673/) (2024)

## Abstract (arXiv)

Cell detection in microscopy images is important to study how cells move and interact with their environment. Most recent deep learning-based methods for cell detection use convolutional neural networks (CNNs). However, inspired by the success in other computer vision applications, vision transformers (ViTs) are also used for this purpose. We propose a novel hybrid CNN-ViT model for cell detection in microscopy images to exploit the advantages of both types of deep learning models. We employ an efficient CNN, that was pre-trained on the ImageNet dataset, to extract image features and utilize transfer learning to reduce the amount of required training data. Extracted image features are further processed by a combination of convolutional and transformer layers, so that the convolutional layers can focus on local information and the transformer layers on global information. Our centroid-based cell detection method represents cells as ellipses and is end-to-end trainable. Furthermore, we show that our proposed model can outperform fully convolutional one-stage detectors on four different 2D microscopy datasets. Code is available at: https://github.com/roydenwa/cell-centroid-former