101 citations · 327 across the 27 of their papers we have counts for
7 papers · 2 filters
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…
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…