1 citations · 1 across the 5 of their papers we have counts for
5 papers
Adaptive Label Error Detection: A Bayesian Approach to Mislabeled Data Detection
Zan Chaudhry, Noam H. Rotenberg, Brian Caffo +2
Machine learning classification systems are susceptible to poor performance when trained with incorrect ground truth labels, even when data is well-curated by expert annotators. As…
Evidential Uncertainty Quantification: A Variance-Based Perspective
Ruxiao Duan, Brian Caffo, Harrison X. Bai +2
Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning. Recent advan…
Applications of Sequential Learning for Medical Image Classification
Sohaib Naim, Brian Caffo, Haris I Sair +1
Purpose: The aim of this work is to develop a neural network training framework for continual training of small amounts of medical imaging data and create heuristics to assess trai…
Stability and Localization of inter-individual differences in functional connectivity
Raag D. Airan, Joshua T. Vogelstein, Jay J. Pillai +3
Much recent attention has been paid to quantifying anatomic and functional neuroimaging on the individual subject level. For optimal individual subject characterization, specific a…
Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models
Chen Yue, Shaojie Chen, Haris I. Sair +2
Data reproducibility is a critical issue in all scientific experiments. In this manuscript, we consider the problem of quantifying the reproducibility of graphical measurements. We…