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

Authors
 Oh, Seung-jae  ;  Bark, Hyeonsang  ;  Maeng, Inhee  ;  Kang, Chul  ;  Kang, Seok-gu  ;  Ryu, Han-cheol  ;  Kim, Sehoon  ;  Ji, Youngbin 
Citation
 Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol.13934, 2025-12 
Article Number
 1393432 
Journal Title
Progress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN
 1605-7422 
Issue Date
2025-12
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.
Full Text
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13934/3097796/Deep-learning-based-AI-model-for-brain-tumor-segmentation-in/10.1117/12.3097796
DOI
10.1117/12.3097796
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/22282913/210480
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