1 citations · 2 across the 6 of their papers we have counts for
7 papers
Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT
Jamie Deng, Yusen Wu, Yelena Yesha +1
Venous thromboembolism (VTE) is a critical cardiovascular condition, encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE). Accurate and timely identification of VTE…
Distributional Drift Detection in Medical Imaging with Sketching and Fine-Tuned Transformer
Yusen Wu, Phuong Nguyen, Rose Yesha +1
Distributional drift detection is important in medical applications as it helps ensure the accuracy and reliability of models by identifying changes in the underlying data distribu…
Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports
Jamie Deng, Yusen Wu, Hilary Hayssen +8
Rapid and accurate identification of Venous thromboembolism (VTE), a severe cardiovascular condition including deep vein thrombosis (DVT) and pulmonary embolism (PE), is important…
Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications
Yusen Wu, Jamie Deng, Hao Chen +2
Federated Learning (FL) has revolutionized how we train deep neural networks by enabling decentralized collaboration while safeguarding sensitive data and improving model performan…
Soft Merging: A Flexible and Robust Soft Model Merging Approach for Enhanced Neural Network Performance
Hao Chen, Yusen Wu, Phuong Nguyen +2
Stochastic Gradient Descent (SGD), a widely used optimization algorithm in deep learning, is often limited to converging to local optima due to the non-convex nature of the problem…
MixNN: A design for protecting deep learning models
Chao Liu, Hao Chen, Yusen Wu +1
In this paper, we propose a novel design, called MixNN, for protecting deep learning model structure and parameters. The layers in a deep learning model of MixNN are fully decentra…
Tolerating Adversarial Attacks and Byzantine Faults in Distributed Machine Learning
Yusen Wu, Hao Chen, Xin Wang +3
Adversarial attacks attempt to disrupt the training, retraining and utilizing of artificial intelligence and machine learning models in large-scale distributed machine learning sys…