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

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Author(s)
Oh, Seung JaeBark, Hyeon SangMaeng, InheeKang, ChulKang, Seok-GuRyu, Han-CheolKim, Se HoonJi, Young Bin
Type
Conference Paper
Citation
2025 European Conference on Biomedical Optics, ECBO 2025
Issued Date
2025-06-22
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)
Publisher
Optical Society of America
Conference Place
GE
Munich
URI
https://scholar.gist.ac.kr/handle/local/33925
Appears in Collections:
Research Institutes > 2. Conference Papers
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