Publications (9)
Federated Vision Transformer with Adaptive Focal Loss for Medical Image Classification
Xinyuan Zhao, Yihang Wu, Ahmad Chaddad +2
While deep learning models like Vision Transformer (ViT) have achieved significant advances, they typically require large datasets. With data privacy regulations, access to many or…
Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labeling
Yunyao Lu, Yihang Wu, Ahmad Chaddad +2
Despite the remarkable performance of supervised medical image segmentation models, relying on a large amount of labeled data is impractical in real-world situations. Semi-supervis…
Deep Modeling and Optimization of Medical Image Classification
Yihang Wu, Muhammad Owais, Reem Kateb +1
Deep models, such as convolutional neural networks (CNNs) and vision transformer (ViT), demonstrate remarkable performance in image classification. However, those deep models requi…
Semi-Supervised Medical Image Segmentation via Dual Networks
Yunyao Lu, Yihang Wu, Reem Kateb +1
Traditional supervised medical image segmentation models require large amounts of labeled data for training; however, obtaining such large-scale labeled datasets in the real world…
Domain Adaptation Techniques for Natural and Medical Image Classification
Ahmad Chaddad, Yihang Wu, Reem Kateb +1
Domain adaptation (DA) techniques have the potential in machine learning to alleviate distribution differences between training and test sets by leveraging information from source…
GMGaze: MoE-Based Context-Aware Gaze Estimation with CLIP and Multiscale Transformer
Xinyuan Zhao, Yihang Wu, Ahmad Chaddad +2
Gaze estimation methods commonly use facial appearances to predict the direction of a person gaze. However, previous studies show three major challenges with convolutional neural n…