Deep Learning-Based Open Source Toolkit for Eosinophil Detection in Pediatric Eosinophilic Esophagitis
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..
| Medienart: |
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
arXiv.org - (2023) vom: 11. Aug. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xiong, Juming [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
000 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
XAR040511472 |
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| 035 | |a (arXiv)2308.06333 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Xiong, Juming |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Deep Learning-Based Open Source Toolkit for Eosinophil Detection in Pediatric Eosinophilic Esophagitis |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a 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. | ||
| 650 | 4 | |a Electrical Engineering and Systems Science - Image and Video Processing |7 (dpeaa)DE-84 | |
| 650 | 4 | |a Computer Science - Computer Vision and Pattern Recognition |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 620 |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 000 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Liu, Yilin |4 aut | |
| 700 | 1 | |a Deng, Ruining |4 aut | |
| 700 | 1 | |a Tyree, Regina N |4 aut | |
| 700 | 1 | |a Correa, Hernan |4 aut | |
| 700 | 1 | |a Hiremath, Girish |4 aut | |
| 700 | 1 | |a Wang, Yaohong |4 aut | |
| 700 | 1 | |a Huo, Yuankai |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t arXiv.org |g (2023) vom: 11. Aug. |
| 773 | 1 | 8 | |g year:2023 |g day:11 |g month:08 |
| 856 | 4 | 0 | |u https://arxiv.org/abs/2308.06333 |z kostenfrei |3 Volltext |
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| 952 | |j 2023 |b 11 |c 08 | ||