Author profile
Department of Radiation Oncology, Stanford University
Biological systems are inherently structured, with genes and molecular components interacting through organized pathways and networks. Graph-based representations are therefore widely used in biological analysis. However, many existing deep learning approache...
Longitudinal brain MRI enables analysis of anatomical changes associated with aging and neurodegenerative disease, yet interpreting structural evolution over time remains challenging due to subtle changes such as inter-subject heterogeneity, and variability a...
High-dimensional genomic and clinical tabular data lack spatial structure, limiting the effectiveness and interpretability of modern deep learning. In medical physics, raw measurements are routinely transformed into structured physical representations (e.g.,...
Medical imaging and molecular omics data provide complementary information about disease. Imaging describes tissue and organ structure, while omics measurements, e.g., gene expression, capture cellular and molecular processes. Integrating these data types is...
Current motion management systems are reactive: they detect patient displacement but require 200-400 ms to respond. This limitation prevents anticipatory intervention during breath-holds, forcing patients to their physiological limits before the system can re...
Conventional radiomics modeling assumes handcrafted features to be independent variables, overlooking structured inter-feature relationships that encode tumor heterogeneity and limit robust generalization across institutions. We propose a novel interaction-aw...