Chronic Wound Image Augmentation and Assessment Using Semi-Supervised Progressive Multi-Granularity EfficientNet-Reference-Cited by-同舟云学术

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Chronic Wound Image Augmentation and Assessment Using Semi-Supervised Progressive Multi-Granularity EfficientNet

Published:2023 Issue: Volume: Page:1-17
ISSN:2644-1276
Container-title:IEEE Open Journal of Engineering in Medicine and Biology
language:
Short-container-title:IEEE Open J. Eng. Med. Biol.

Author:

Liu Ziyang¹^ORCID,Agu Emmanuel¹^ORCID,Pedersen Peder²^ORCID,Lindsay Clifford³,Tulu Bengisu⁴^ORCID,Strong Diane⁴^ORCID

Affiliation:

1. Computer Science Department, Worcester Polytechnic Institute, Worcester, MA, USA

2. Electrical and Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA, USA

3. Department of Radiology, University of Massachusetts Medical School, Worcester, USA

4. Foisie Business School, Worcester Polytechnic Institute, Worcester, MA, USA

Funder

National Institutes of Health

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/8782705/8937520/10050724.pdf?arnumber=10050724

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3. Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images;Journal of Medical Systems;2024-01-16

4. Enhancing Medical Imaging Through Data Augmentation: A Review;Lecture Notes in Computer Science;2024

5. Diyabetik Ayağın Derin Öğrenme Yöntemleriyle Ayırıcı Tanısı;Recep Tayyip Erdoğan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi;2023-12-31

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