# PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnostics

arXiv: https://arxiv.org/abs/2103.02015 · HTML: https://mlsanity.com/paper/2103.02015/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Semantic Segmentation using Adversarial Networks](https://mlsanity.com/paper/1611.08408/) (2016)
- [High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks](https://mlsanity.com/paper/1703.07047/) (2017)
- [A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images](https://mlsanity.com/paper/1803.02786/) (2018)
- [Adapting Mask-RCNN for Automatic Nucleus Segmentation](https://mlsanity.com/paper/1805.00500/) (2018)
- [Machine learning approach for biopsy-based identification of eosinophilic esophagitis reveals importance of global features](https://mlsanity.com/paper/2101.04989/) (2021)

## Built on by (most cited first, 5 of 5)

- [Safety-compliant Generative Adversarial Networks for Human Trajectory Forecasting](https://mlsanity.com/paper/2209.12243/) (2022)
- [Joint Pedestrian Trajectory Prediction through Posterior Sampling](https://mlsanity.com/paper/2404.00237/) (2024)
- [Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation](https://mlsanity.com/paper/2308.08974/) (2023)
- [Cross-organ Deployment of EOS Detection AI without Retraining: Feasibility and Limitation](https://mlsanity.com/paper/2411.15942/) (2024)
- [Trustworthy Pedestrian Trajectory Prediction via Pattern-Aware Interaction Modeling](https://mlsanity.com/paper/2507.13397/) (2025)

## Abstract (arXiv)

Background. Eosinophilic esophagitis (EoE) is an allergic inflammatory condition of the esophagus associated with elevated numbers of eosinophils. Disease diagnosis and monitoring requires determining the concentration of eosinophils in esophageal biopsies, a time-consuming, tedious and somewhat subjective task currently performed by pathologists. Methods. Herein, we aimed to use machine learning to identify, quantitate and diagnose EoE. We labeled more than 100M pixels of 4345 images obtained by scanning whole slides of H&E-stained sections of esophageal biopsies derived from 23 EoE patients. We used this dataset to train a multi-label segmentation deep network. To validate the network, we examined a replication cohort of 1089 whole slide images from 419 patients derived from multiple institutions. Findings. PECNet segmented both intact and not-intact eosinophils with a mean intersection over union (mIoU) of 0.93. This segmentation was able to quantitate intact eosinophils with a mean absolute error of 0.611 eosinophils and classify EoE disease activity with an accuracy of 98.5%. Using whole slide images from the validation cohort, PECNet achieved an accuracy of 94.8%, sensitivity of 94.3%, and specificity of 95.14% in reporting EoE disease activity. Interpretation. We have developed a deep learning multi-label semantic segmentation network that successfully addresses two of the main challenges in EoE diagnostics and digital pathology, the need to detect several types of small features simultaneously and the ability to analyze whole slides efficiently. Our results pave the way for an automated diagnosis of EoE and can be utilized for other conditions with similar challenges.