From the 1 of 5 linked papers with an AI index.
1 citations · 1 across the 4 of their papers we have counts for
5 papers
Bias Correction for Relative Importance Measures via Doubly Stochastic Reallocation
Tien-En Chang, Argon Chen
The paper analyzes bias in relative importance measures for linear regression, proposes correcting the Green–Carroll–DeSarbo measure by converting its reallocation matrix to a doub…
OphMAE: Bridging Volumetric and Planar Imaging with a Foundation Model for Adaptive Ophthalmological Diagnosis
Tienyu Chang, Zhen Chen, Renjie Liang +9
The advent of foundation models has heralded a new era in medical artificial intelligence (AI), enabling the extraction of generalizable representations from large-scale unlabeled…
SAIL: Structure-Aware Interpretable Learning for Anatomy-Aligned Post-hoc Explanations in OCT
Tienyu Chang, Tianhao Li, Ruogu Fang +2
Optical coherence tomography (OCT), a commonly used retinal imaging modality, plays a central role in retinal disease diagnosis by providing high-resolution visualization of retina…
Variable Selection Using Relative Importance Rankings
Tien-En Chang, Argon Chen
Although conceptually related, variable selection and relative importance (RI) analysis have been treated quite differently in the literature. While RI is typically used for post-h…
Understanding and Using the Relative Importance Measures Based on Orthogonalization and Reallocation
Tien-En Chang, Argon Chen
A class of relative importance measures based on orthogonalization and reallocation, ORMs, has been found to effectively approximate the General Dominance index (GD). In particular…