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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, HyeonsangMaeng, InheeKang, ChulKang, Seok-guRyu, Han-cheolKim, SehoonJi, Youngbin
Type
Conference Paper
Citation
4th Translational Biophotonics: Diagnostics and Therapeutics
Issued Date
2025-06-26
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
SPIE
Conference Place
GE
Munich
URI
https://scholar.gist.ac.kr/handle/local/33525
Appears in Collections:
Research Institutes > 2. Conference Papers
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