347 citations · 610 across the 24 of their papers we have counts for
9 papers · 1 filter
Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data
Yifei Huang, Xiaoxiao Li, Lijin Yang +6
The human gaze is a cost-efficient physiological data that reveals human underlying attentional patterns. The selective attention mechanism helps the cognition system focus on task…
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction
Liang Peng, Nan Wang, Nicha Dvornek +2
Graph Convolutional Neural Networks (GCNs) are widely used for graph analysis. Specifically, in medical applications, GCNs can be used for disease prediction on a population graph,…
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition
Li Fu, Xiaoxiao Li, Runyu Wang +5
End-to-end Automatic Speech Recognition (ASR) models are usually trained to optimize the loss of the whole token sequence, while neglecting explicit phonemic-granularity supervisio…
EMA: Auditing Data Removal from Trained Models
Yangsibo Huang, Xiaoxiao Li, Kai Li
Data auditing is a process to verify whether certain data have been removed from a trained model. A recently proposed method (Liu et al. 20) uses Kolmogorov-Smirnov (KS) distance f…
BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis
Hejie Cui, Wei Dai, Yanqiao Zhu +3
Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience. GNNs are promising to model complicated network data, but they are…
One Map Does Not Fit All: Evaluating Saliency Map Explanation on Multi-Modal Medical Images
Weina Jin, Xiaoxiao Li, Ghassan Hamarneh
Being able to explain the prediction to clinical end-users is a necessity to leverage the power of AI models for clinical decision support. For medical images, saliency maps are th…
Subgraph Federated Learning with Missing Neighbor Generation
Ke Zhang, Carl Yang, Xiaoxiao Li +2
Graphs have been widely used in data mining and machine learning due to their unique representation of real-world objects and their interactions. As graphs are getting bigger and b…
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis
Baihe Huang, Xiaoxiao Li, Zhao Song +1
Federated Learning (FL) is an emerging learning scheme that allows different distributed clients to train deep neural networks together without data sharing. Neural networks have b…
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang +2
The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improv…