# Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images

arXiv: https://arxiv.org/abs/2011.03683 · HTML: https://mlsanity.com/paper/2011.03683/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2020.

## Built on by (most cited first, 12 of 12)

- [DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2202.00972/) (2022)
- [Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps](https://mlsanity.com/paper/2102.11865/) (2021)
- [Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification](https://mlsanity.com/paper/2209.13844/) (2022)
- [Plant Taxonomy Meets Plant Counting: A Fine-Grained, Taxonomic Dataset for Counting Hundreds of Plant Species](https://mlsanity.com/paper/2603.21229/) (2026)
- [A novel adversarial learning strategy for medical image classification](https://mlsanity.com/paper/2206.11501/) (2022)
- [CellFMCount: A Fluorescence Microscopy Dataset, Benchmark, and Methods for Cell Counting](https://mlsanity.com/paper/2511.19351/) (2025)
- [Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps](https://mlsanity.com/paper/2210.07102/) (2022)
- [Vision Transformers for Weakly-Supervised Microorganism Enumeration](https://mlsanity.com/paper/2412.02250/) (2024)
- [Leveraging Weak Supervision for Cell Localization in Digital Pathology Using Multitask Learning and Consistency Loss](https://mlsanity.com/paper/2412.15392/) (2024)
- [Towards Conditional Feature Alignment for Cross-Domain Counting](https://mlsanity.com/paper/2506.17137/) (2025)
- [Rank-Aware Agglomeration of Foundation Models for Immunohistochemistry Image Cell Counting](https://mlsanity.com/paper/2511.12588/) (2025)
- [Object Counting Across Modalities: Taxonomies, Benchmarks, Applications, and Open Challenges](https://mlsanity.com/paper/2608.23845/) (2026)

## Abstract (arXiv)

Accurately counting the number of cells in microscopy images is required in many medical diagnosis and biological studies. This task is tedious, time-consuming, and prone to subjective errors. However, designing automatic counting methods remains challenging due to low image contrast, complex background, large variance in cell shapes and counts, and significant cell occlusions in two-dimensional microscopy images. In this study, we proposed a new density regression-based method for automatically counting cells in microscopy images. The proposed method processes two innovations compared to other state-of-the-art density regression-based methods. First, the density regression model (DRM) is designed as a concatenated fully convolutional regression network (C-FCRN) to employ multi-scale image features for the estimation of cell density maps from given images. Second, auxiliary convolutional neural networks (AuxCNNs) are employed to assist in the training of intermediate layers of the designed C-FCRN to improve the DRM performance on unseen datasets. Experimental studies evaluated on four datasets demonstrate the superior performance of the proposed method.