101 citations · 167 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
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…
Generalizable and Explainable Deep Learning for Medical Image Computing: An Overview
Ahmad Chaddad, Yan Hu, Yihang Wu +2
Objective. This paper presents an overview of generalizable and explainable artificial intelligence (XAI) in deep learning (DL) for medical imaging, aimed at addressing the urgent…
FAA-CLIP: Federated Adversarial Adaptation of CLIP
Yihang Wu, Ahmad Chaddad, Christian Desrosiers +2
Despite the remarkable performance of vision language models (VLMs) such as Contrastive Language Image Pre-training (CLIP), the large size of these models is a considerable obstacl…
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
Ahmad Chaddad, Qizong lu, Jiali Li +5
Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in each domain is driven by a growing body of annotated data, increased computational re…