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Deep Learning-Based AI Model for Brain Tumor Segmentation in Digital Pathology and Terahertz Imaging

Authors
 Oh, Seung Jae  ;  Bark, Hyeon Sang  ;  Maeng, Inhee  ;  Kang, Chul  ;  Kang, Seok-Gu  ;  Ryu, Han-Cheol  ;  Kim, Se Hoon  ;  Ji, Young Bin 
Citation
 European Conference on Biomedical Optics, ECBO 2025, 2025-06 
Journal Title
 European Conference on Biomedical Optics, ECBO 2025 
Issue Date
2025-06
Abstract
This study presents a deep learning-based AI model for brain tumor segmentation in digital pathology images. Using a transgenic mouse model and H&E-stained images, we developed and trained the model with DEEP:PHI, employing U-Net and attention U-Net architectures. The AI model facilitates accurate cancer detection, contributing to terahertz imaging-based diagnostics and enhancing real-time surgical decision-making with minimal pathologist intervention. © 2025 The Author(s)
Full Text
https://opg.optica.org/abstract.cfm?URI=ECBO-2025-M3A.11
DOI
10.1364/ECBO.2025.M3A.11
Appears in Collections:
1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
Yonsei Authors
Kang, Seok Gu(강석구)
Kim, Se Hoon(김세훈) ORCID logo https://orcid.org/0000-0001-7516-7372
Maeng, In hee(맹인희)
Oh, Seung Jae(오승재)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/212315
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