3 citations · 5 across the 8 of their papers we have counts for
8 papers
Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations
Shaoyan Pan, Yiqiao Liu, Sarah Halek +6
In oncology research, accurate 3D segmentation of lesions from CT scans is essential for the modeling of lesion growth kinetics. However, following the RECIST criteria, radiologist…
Target alignment in truncated kernel ridge regression
Arash A. Amini, Richard Baumgartner, Dai Feng
Kernel ridge regression (KRR) has recently attracted renewed interest due to its potential for explaining the transient effects, such as double descent, that emerge during neural n…
A Framework for an Assessment of the Kernel-target Alignment in Tree Ensemble Kernel Learning
Dai Feng, Richard Baumgartner
Kernels ensuing from tree ensembles such as random forest (RF) or gradient boosted trees (GBT), when used for kernel learning, have been shown to be competitive to their respective…
Parmsurv: a SAS Macro for Flexible Parametric Survival Analysis with Long-Term Predictions
Han Fu, Shahrul Mt-Isa, Richard Baumgartner +1
Health economic evaluations often require predictions of survival rates beyond the follow-up period. Parametric survival models can be more convenient for economic modelling than t…
(Decision and regression) tree ensemble based kernels for regression and classification
Dai Feng, Richard Baumgartner
Tree based ensembles such as Breiman's random forest (RF) and Gradient Boosted Trees (GBT) can be interpreted as implicit kernel generators, where the ensuing proximity matrix repr…
Random Forest (RF) Kernel for Regression, Classification and Survival
Dai Feng, Richard Baumgartner
Breiman's random forest (RF) can be interpreted as an implicit kernel generator,where the ensuing proximity matrix represents the data-driven RF kernel. Kernel perspective on the R…
A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials
Antong Chen, Jennifer Saouaf, Bo Zhou +6
Herein we propose a deep learning-based approach for the prediction of lung lesion response based on radiomic features extracted from clinical CT scans of patients in non-small cel…
SMART: A statistical framework for optimal design matrix generation with application to fMRI
Gautam Pendse, Adam Schwarz, Richard Baumgartner +3
The general linear model (GLM) is a well established tool for analyzing functional magnetic resonance imaging (fMRI) data. Most fMRI analyses via GLM proceed in a massively univari…