11 citations · 30 across the 10 of their papers we have counts for
10 papers
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning
Guangyu Sun, Matias Mendieta, Jun Luo +2
Personalized Federated Learning (PFL) represents a promising solution for decentralized learning in heterogeneous data environments. Partial model personalization has been proposed…
Human not in the loop: objective sample difficulty measures for Curriculum Learning
Zhengbo Zhou, Jun Luo, Dooman Arefan +2
Curriculum learning is a learning method that trains models in a meaningful order from easier to harder samples. A key here is to devise automatic and objective difficulty measures…
Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning
Saba Dadsetan, Mohsen Hejrati, Shandong Wu +1
Developing successful artificial intelligence systems in practice depends on both robust deep learning models and large, high-quality data. However, acquiring and labeling data can…
Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification
Long Gao, Chang Liu, Dooman Arefan +3
Deep learning models have gained remarkable performance on a variety of image classification tasks. However, many models suffer from limited performance in clinical or medical sett…
Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images
Long Gao, Chang Liu, Dooman Arefan +2
Medical image data are usually imbalanced across different classes. One-class classification has attracted increasing attention to address the data imbalance problem by distinguish…
Deep Curriculum Learning in Task Space for Multi-Class Based Mammography Diagnosis
Jun Luo, Dooman Arefan, Margarita Zuley +2
Mammography is used as a standard screening procedure for the potential patients of breast cancer. Over the past decade, it has been shown that deep learning techniques have succee…
Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture Classification
Jun Luo, Gene Kitamura, Dooman Arefan +3
Elbow fracture diagnosis often requires patients to take both frontal and lateral views of elbow X-ray radiographs. In this paper, we propose a multiview deep learning method for a…
Medical Knowledge-Guided Deep Curriculum Learning for Elbow Fracture Diagnosis from X-Ray Images
Jun Luo, Gene Kitamura, Emine Doganay +2
Elbow fractures are one of the most common fracture types. Diagnoses on elbow fractures often need the help of radiographic imaging to be read and analyzed by a specialized radiolo…
FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification
Jun Luo, Shandong Wu
Machine learning in medical research, by nature, needs careful attention on obeying the regulations of data privacy, making it difficult to train a machine learning model over gath…
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning
Jun Luo, Shandong Wu
Conventional federated learning (FL) trains one global model for a federation of clients with decentralized data, reducing the privacy risk of centralized training. However, the di…