4 papers · 1 filter
Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Components
Ling Zhan, Xiaoyao Yu, Tao Jia
AI for Science (AI4Science) workflows often treat the released dataset as a fixed interface to the underlying system. However, in domains relying on \emph{indirect observation}, th…
Accelerating Benchmarking of Functional Connectivity Modeling via Structure-aware Core-set Selection
Ling Zhan, Zhen Li, Junjie Huang +1
Benchmarking the hundreds of functional connectivity (FC) modeling methods on large-scale fMRI datasets is critical for reproducible neuroscience. However, the combinatorial explos…
Beyond Pairwise Connections: Extracting High-Order Functional Brain Network Structures under Global Constraints
Ling Zhan, Junjie Huang, Xiaoyao Yu +2
Functional brain network (FBN) modeling often relies on local pairwise interactions, whose limitation in capturing high-order dependencies is theoretically analyzed in this paper.…
Multi-feature concatenation and multi-classifier stacking: an interpretable and generalizable machine learning method for MDD discrimination with rsfMRI
Yunsong Luo, Wenyu Chen, Ling Zhan +2
Major depressive disorder is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspect…