2 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
Enhancing binary classification: A new stacking method via leveraging computational geometry
Wei Wu, Liang Tang, Zhongjie Zhao +1
Stacking, a potent ensemble learning method, leverages a meta-model to harness the strengths of multiple base models, thereby enhancing prediction accuracy. Traditional stacking te…
A Quantitative Approach for Evaluating Disease Focus and Interpretability of Deep Learning Models for Alzheimer's Disease Classification
Thomas Yu Chow Tam, Litian Liang, Ke Chen +2
Deep learning (DL) models have shown significant potential in Alzheimer's Disease (AD) classification. However, understanding and interpreting these models remains challenging, whi…
Quantitative Evaluation of the Saliency Map for Alzheimer's Disease Classifier with Anatomical Segmentation
Yihan Zhang, Xuanshuo Zhang, Wei Wu +1
Saliency maps have been widely used to interpret deep learning classifiers for Alzheimer's disease (AD). However, since AD is heterogeneous and has multiple subtypes, the pathologi…
Demographic Bias of Expert-Level Vision-Language Foundation Models in Medical Imaging
Yuzhe Yang, Yujia Liu, Xin Liu +6
Advances in artificial intelligence (AI) have achieved expert-level performance in medical imaging applications. Notably, self-supervised vision-language foundation models can dete…