Cheng Huang

This dissertation integrates multimodal retinal imaging, including fundus photography, OCT, and OCTA, to build a suite of deep learning frameworks spanning vessel segmentation networks, biomarker discovery pipelines, and multimodal fusion models for detecting early glaucomatous changes with high precision, robustness, and interpretability, and contributes several curated datasets including GSS-RetVein as standardized benchmarks for cross-domain validation, reporting that the proposed models outperform existing state-of-the-art approaches across multiple public and clinical datasets with marked improvements in diagnostic sensitivity and specificity.

This dissertation integrates multimodal retinal imaging, including fundus photography, OCT, and OCTA, to build a suite of deep learning frameworks spanning vessel segmentation networks, biomarker discovery pipelines, and multimodal fusion models for detecting early glaucomatous changes with high precision, robustness, and interpretability, and contributes several curated datasets including GSS-RetVein as standardized benchmarks for cross-domain validation, reporting that the proposed models outperform existing state-of-the-art approaches across multiple public and clinical datasets with marked improvements in diagnostic sensitivity and specificity.