# Automatic Lymphocyte Detection in H&E Images with Deep Neural Networks

arXiv: https://arxiv.org/abs/1612.03217 · HTML: https://mlsanity.com/paper/1612.03217/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2016.

## Built on by (most cited first, 4 of 4)

- [CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images](https://mlsanity.com/paper/2305.09211/) (2023)
- [Synthetic Generation of Three-Dimensional Cancer Cell Models from Histopathological Images](https://mlsanity.com/paper/2101.11600/) (2021)
- [Neuroplastic graph attention networks for nuclei segmentation in histopathology images](https://mlsanity.com/paper/2201.03669/) (2022)
- [What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2201.08550/) (2022)

## Abstract (arXiv)

Automatic detection of lymphocyte in H&E images is a necessary first step in lots of tissue image analysis algorithms. An accurate and robust automated lymphocyte detection approach is of great importance in both computer science and clinical studies. Most of the existing approaches for lymphocyte detection are based on traditional image processing algorithms and/or classic machine learning methods. In the recent years, deep learning techniques have fundamentally transformed the way that a computer interprets images and have become a matchless solution in various pattern recognition problems. In this work, we design a new deep neural network model which extends the fully convolutional network by combining the ideas in several recent techniques, such as shortcut links. Also, we design a new training scheme taking the prior knowledge about lymphocytes into consideration. The training scheme not only efficiently exploits the limited amount of free-form annotations from pathologists, but also naturally supports efficient fine-tuning. As a consequence, our model has the potential of self-improvement by leveraging the errors collected during real applications. Our experiments show that our deep neural network model achieves good performance in the images of different staining conditions or different types of tissues.