45 citations · 91 across the 16 of their papers we have counts for
16 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…
Impact of domain adaptation in deep learning for medical image classifications
Yihang Wu, Ahmad Chaddad
Domain adaptation (DA) is a quickly expanding area in machine learning that involves adjusting a model trained in one domain to perform well in another domain. While there have bee…
Deep Modeling and Interpretation for Bladder Cancer Classification
Ahmad Chaddad, Yihang Wu, Xianrui Chen
Deep models based on vision transformer (ViT) and convolutional neural network (CNN) have demonstrated remarkable performance on natural datasets. However, these models may not be…
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…
Federated CLIP for Resource-Efficient Heterogeneous Medical Image Classification
Yihang Wu, Ahmad Chaddad
Despite the remarkable performance of deep models in medical imaging, they still require source data for training, which limits their potential in light of privacy concerns. Federa…
Towards a Transparent and Interpretable AI Model for Medical Image Classifications
Binbin Wen, Yihang Wu, Tareef Daqqaq +1
The integration of artificial intelligence (AI) into medicine is remarkable, offering advanced diagnostic and therapeutic possibilities. However, the inherent opacity of complex AI…
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…
Classification based deep learning models for lung cancer and disease using medical images
Ahmad Chaddad, Jihao Peng, Yihang Wu
The use of deep learning (DL) in medical image analysis has significantly improved the ability to predict lung cancer. In this study, we introduce a novel deep convolutional neural…
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…
A Knowledge Distillation-Based Approach to Enhance Transparency of Classifier Models
Yuchen Jiang, Xinyuan Zhao, Yihang Wu +1
With the rapid development of artificial intelligence (AI), especially in the medical field, the need for its explainability has grown. In medical image analysis, a high degree of…
Simulations of Common Unsupervised Domain Adaptation Algorithms for Image Classification
Ahmad Chaddad, Yihang Wu, Yuchen Jiang +2
Traditional machine learning assumes that training and test sets are derived from the same distribution; however, this assumption does not always hold in practical applications. Th…
FACMIC: Federated Adaptative CLIP Model for Medical Image Classification
Yihang Wu, Christian Desrosiers, Ahmad Chaddad
Federated learning (FL) has emerged as a promising approach to medical image analysis that allows deep model training using decentralized data while ensuring data privacy. However,…