45 citations · 91 across the 16 of their papers we have counts for
11 papers · 1 filter
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…