106 citations · 106 across the 4 of their papers we have counts for
5 papers
Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands
Vasiliki Tassopoulou, Charis Stamouli, Haochang Shou +2
Despite recent progress in predicting biomarker trajectories from real clinical data, uncertainty in the predictions poses high-stakes risks (e.g., misdiagnosis) that limit their c…
Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories
Vasiliki Tassopoulou, Haochang Shou, Christos Davatzikos
Longitudinal biomedical studies monitor individuals over time to capture dynamics in brain development, disease progression, and treatment effects. However, estimating trajectories…
Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples
Sai Spandana Chintapalli, Rongguang Wang, Zhijian Yang +7
Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. For successful application of machine learning techniques for disea…
Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience
Rongguang Wang, Vishnu Bashyam, Zhijian Yang +10
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to a broader family called…
Enhancing Handwritten Text Recognition with N-gram sequence decomposition and Multitask Learning
Vasiliki Tassopoulou, George Retsinas, Petros Maragos
Current state-of-the-art approaches in the field of Handwritten Text Recognition are predominately single task with unigram, character level target units. In our work, we utilize a…