Deep Learning-Based Open Source Toolkit for Eosinophil Detection in
  Pediatric Eosinophilic Esophagitis
Juming Xiong Yilin Liu Ruining Deng Regina N Tyree Hernan Correa Girish Hiremath Yaohong Wang Yuankai Huo
Eosinophilic Esophagitis (EoE) is a chronic, immune/antigen-mediated
esophageal disease, characterized by symptoms related to esophageal dysfunction
and histological evidence of eosinophil-dominant inflammation. Owing to the
intricate microscopic representation of EoE in imaging, current methodologies
which depend on manual identification are not only labor-intensive but also
prone to inaccuracies. In this study, we develop an open-source toolkit, named
Open-EoE, to perform end-to-end whole slide image (WSI) level eosinophil (Eos)
detection using one line of command via Docker. Specifically, the toolkit
supports three state-of-the-art deep learning-based object detection models.
Furthermore, Open-EoE further optimizes the performance by implementing an
ensemble learning strategy, and enhancing the precision and reliability of our
results. The experimental results demonstrated that the Open-EoE toolkit can
efficiently detect Eos on a testing set with 289 WSIs. At the widely accepted
threshold of >= 15 Eos per high power field (HPF) for diagnosing EoE, the
Open-EoE achieved an accuracy of 91%, showing decent consistency with
pathologist evaluations. This suggests a promising avenue for integrating
machine learning methodologies into the diagnostic process for EoE. The docker
and source code has been made publicly available at
https://github.com/hrlblab/Open-EoE.