7 citations · 43 across the 22 of their papers we have counts for
8 papers · 1 filter
FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation
Amirhossein Kazerouni, Sanaz Karimijafarbigloo, Reza Azad +3
Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training. In response to this challenge, we introduce Fu…
Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning
Sanaz Karimijafarbigloo, Reza Azad, Yury Velichko +2
Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-la…
HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling
Afshin Bozorgpour, Bobby Azad, Reza Azad +3
Accurate and automated segmentation of intervertebral discs (IVDs) in medical images is crucial for assessing spine-related disorders, such as osteoporosis, vertebral fractures, or…
INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings
Amirhossein Kazerouni, Reza Azad, Alireza Hosseini +2
Implicit Neural Representations (INRs) have revolutionized signal representation by leveraging neural networks to provide continuous and smooth representations of complex data. How…
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Ariana M. Familiar, Anahita Fathi Kazerooni, Hannah Anderson +37
Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the…
Self-supervised Semantic Segmentation: Consistency over Transformation
Sanaz Karimijafarbigloo, Reza Azad, Amirhossein Kazerouni +3
Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures. However, prevailing supervised deep learning approaches for medical…
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation
Reza Azad, Leon Niggemeier, Michael Huttemann +5
Medical image segmentation has seen significant improvements with transformer models, which excel in grasping far-reaching contexts and global contextual information. However, the…
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection
Reza Azad, Amirhossein Kazerouni, Babak Azad +4
Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks. However, compared to the Convolutional Neural Network (CNN) models, it ha…