# Cross-organ Deployment of EOS Detection AI without Retraining: Feasibility and Limitation

arXiv: https://arxiv.org/abs/2411.15942 · HTML: https://mlsanity.com/paper/2411.15942/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 24 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Deep Snake for Real-Time Instance Segmentation](https://mlsanity.com/paper/2001.01629/) (2020)
- [CircleNet: Anchor-free Detection with Circle Representation](https://mlsanity.com/paper/2006.02474/) (2020)
- [Circle Representation for Medical Object Detection](https://mlsanity.com/paper/2110.12093/) (2021)
- [Machine learning approach for biopsy-based identification of eosinophilic esophagitis reveals importance of global features](https://mlsanity.com/paper/2101.04989/) (2021)
- [PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnostics](https://mlsanity.com/paper/2103.02015/) (2021)
- [CircleSnake: Instance Segmentation with Circle Representation](https://mlsanity.com/paper/2211.01254/) (2022)
- [Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation](https://mlsanity.com/paper/2308.08974/) (2023)
- [Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations](https://mlsanity.com/paper/1906.09549/) (2019)

## Abstract (arXiv)

Chronic rhinosinusitis (CRS) is characterized by persistent inflammation in the paranasal sinuses, leading to typical symptoms of nasal congestion, facial pressure, olfactory dysfunction, and discolored nasal drainage, which can significantly impact quality-of-life. Eosinophils (Eos), a crucial component in the mucosal immune response, have been linked to disease severity in CRS. The diagnosis of eosinophilic CRS typically uses a threshold of 10-20 eos per high-power field (HPF). However, manually counting Eos in histological samples is laborious and time-intensive, making the use of AI-driven methods for automated evaluations highly desirable. Interestingly, eosinophils are predominantly located in the gastrointestinal (GI) tract, which has prompted the release of numerous deep learning models trained on GI data. This study leverages a CircleSnake model initially trained on upper-GI data to segment Eos cells in whole slide images (WSIs) of nasal tissues. It aims to determine the extent to which Eos segmentation models developed for the GI tract can be adapted to nasal applications without retraining. The experimental results show promising accuracy in some WSIs, although, unsurprisingly, the performance varies across cases. This paper details these performance outcomes, delves into the reasons for such variations, and aims to provide insights that could guide future development of deep learning models for eosinophilic CRS.