Deep Learning-Based AI Model for Brain Tumor Segmentation in Digital Pathology and Terahertz Imaging
- Author(s)
- Oh, Seung-jae; Bark, Hyeonsang; Maeng, Inhee; Kang, Chul; Kang, Seok-gu; Ryu, Han-cheol; Kim, Sehoon; Ji, 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
- 공개 및 라이선스
-
- 파일 목록
-
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.