研究紹介
研究テーマ
Integrating Raman hyperspectral imaging with advanced AI for the label-free identification hydrogel-induced Cancer Stem Cells (CSCs), advancing next-generation cancer diagnosis and drug development.
キーワード
研究概要
My research focuses on developing AI-driven diagnostic tools that integrate Raman microscopic imaging, machine learning, and information theory. During my doctoral work at Hokkaido University, I analyzed Raman hyperspectral images of rat liver tissues from a dietary model of nonalcoholic fatty liver disease (NAFLD) and introduced several data-driven methods, including SLIC-based segmentation, Rate-Distortion-Theory–based clustering, and Random Forest feature selection to enhance spectral interpretation and tissue classification. My future goal is to establish AI-driven, Raman imaging–based label-free diagnostic methods by applying deep learning, bioinformatics, and advanced computational modeling.
代表的な研究成果
- Raman imaging of rat nonalcoholic fatty liver tissues reveals distinct biomolecular states Helal, K. M., Cahyadi, H., Taylor, J. N., et al., FEBS Letters, 2023, 597 (11), 1517-1527
DOI: 10.1002/1873-3468.14600 - Raman spectroscopic histology using machine learning for nonalcoholic fatty liver disease Helal, K. M., Taylor, J. N., Cahyadi, H., et al., FEBS letters, 2019, 593 (18), 2535-2544
DOI: 10.1002/1873-3468.13520