Machine learning method for the cellular phenotyping of nasal polyps from multicentre tissue scans-Reference-Cited by-同舟云学术

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Machine learning method for the cellular phenotyping of nasal polyps from multicentre tissue scans

Published:2023-04-28 Issue:8 Volume:19 Page:1023-1028
ISSN:1744-666X
Container-title:Expert Review of Clinical Immunology
language:en
Short-container-title:Expert Review of Clinical Immunology

Author:

Ding Jing¹²,Yue Changli¹²,Wang Chengshuo³,Liu Wei⁴,Zhang Libo⁴,Chen Bo⁴,Shen Shen³,Piao Yingshi¹²,Zhang Luo³

Affiliation:

1. Department of Pathology, Beijing Tongren Hospital, Capital Medical University, Beijing, China

2. Beijing Key Laboratory of Head and Neck Molecular Pathological Diagnosis, Beijing Tongren Hospital, Capital Medical University, Beijing, China

3. Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China

4. Department of Center for Translational Medicine, Keymed Biosciences Inc, Chengdu, Sichuan, China

Funder

national key R&D program of China

Publisher

Informa UK Limited

Subject

Immunology,Immunology and Allergy

Link

https://www.tandfonline.com/doi/pdf/10.1080/1744666X.2023.2207824

Reference17 articles.

1. Epidemiology of chronic rhinosinusitis: results from a cross‐sectional survey in seven C hinese cities

2. Distinct features of chronic rhinosinusitis with and without nasal polyps

3. Chronic Rhinosinusitis with Nasal Polyps

4. Chronic rhinosinusitis and nasal polyps: the role of generic and specific questionnaires on assessing its impact on patient’s quality of life

5. Novel scoring system and algorithm for classifying chronic rhinosinusitis: the JESREC Study

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2. Automated Colorectal Polyps Detection from Endoscopic Images using MultiResUNet Framework with Attention Guided Segmentation;Human-Centric Intelligent Systems;2024-04-08

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