activity
20182021
most citedJointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI

35 citations · 54 across the 6 of their papers we have counts for

collaborators

16 papers

cs.DC2021

On the Fairness of Swarm Learning in Skin Lesion Classification

Di Fan, Yifan Wu, Xiaoxiao Li

in healthcare. However, the existing AI model may be biased in its decision marking. The bias induced by data itself, such as collecting data in subgroups only, can be mitigated by…

cs.LG20212 cited

Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity

Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang +2

Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain. We propose to model heter…

cs.CV2021

Estimating and Improving Fairness with Adversarial Learning

Xiaoxiao Li, Ziteng Cui, Yifan Wu +2

Fairness and accountability are two essential pillars for trustworthy Artificial Intelligence (AI) in healthcare. However, the existing AI model may be biased in its decision marki…

cs.CV2020

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis

Xiaoxiao Li, Yuan Zhou, Nicha C. Dvornek +4

Understanding how certain brain regions relate to a specific neurological disorder has been an important area of neuroimaging research. A promising approach to identify the salient…

stat.ML2020

Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE

Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li +3

Neural ordinary differential equations (NODEs) have recently attracted increasing attention; however, their empirical performance on benchmark tasks (e.g. image classification) are…

cs.LG2020

Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results

Xiaoxiao Li, Yufeng Gu, Nicha Dvornek +3

Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggreg…