1 citations · 1 across the 5 of their papers we have counts for
5 papers
Bias Correction for Relative Importance Measures via Doubly Stochastic Reallocation
Tien-En Chang, Argon Chen
Relative importance (RI) analysis quantifies each predictor's contribution to the explained variance of a linear model. General Dominance (GD), a widely used benchmark, requires ev…
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…
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…
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…