134 citations · 135 across the 6 of their papers we have counts for
5 papers · 1 filter
OSCS-SupCon: Orthogonal Sigmoid-based Common and Style Supervised Contrastive Learning for Robust Feature Disentanglement
Bin Wang, Fadi Dornaika
Supervised Contrastive Learning (SupCon) has achieved strong performance by explicitly modeling pairwise relationships among samples. However, existing SupCon-based methods suffer…
Decoding Matters: Efficient Mamba-Based Decoder with Distribution-Aware Deep Supervision for Medical Image Segmentation
Fares Bougourzi, Fadi Dornaika, Abdenour Hadid
Deep learning has achieved remarkable success in medical image segmentation, often reaching expert-level accuracy in delineating tumors and tissues. However, most existing approach…
MCFCN: Multi-View Clustering via a Fusion-Consensus Graph Convolutional Network
Chenping Pei, Fadi Dornaika, Jingjun Bi
Existing Multi-view Clustering (MVC) methods based on subspace learning focus on consensus representation learning while neglecting the inherent topological structure of data. Desp…
A Re-node Self-training Approach for Deep Graph-based Semi-supervised Classification on Multi-view Image Data
Jingjun Bi, Fadi Dornaika
Recently, graph-based semi-supervised learning and pseudo-labeling have gained attention due to their effectiveness in reducing the need for extensive data annotations. Pseudo-labe…
SegDT: A Diffusion Transformer-Based Segmentation Model for Medical Imaging
Salah Eddine Bekhouche, Gaby Maroun, Fadi Dornaika +1
Medical image segmentation is crucial for many healthcare tasks, including disease diagnosis and treatment planning. One key area is the segmentation of skin lesions, which is vita…