48 citations · 77 across the 18 of their papers we have counts for
6 papers · 1 filter
Diffusion Models for Medical Image Analysis: A Comprehensive Survey
Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Moein Heidari +4
Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward…
TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation
Reza Azad, Moein Heidari, Moein Shariatnia +4
Convolutional neural networks (CNNs) have been the de facto standard in a diverse set of computer vision tasks for many years. Especially, deep neural networks based on seminal arc…
TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model
Reza Azad, Mohammad T. AL-Antary, Moein Heidari +1
In the past few years, convolutional neural networks (CNNs), particularly U-Net, have been the prevailing technique in the medical image processing era. Specifically, the seminal U…
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
Moein Heidari, Amirhossein Kazerouni, Milad Soltany +4
Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation in modeling long-range dependencies and…
Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach
Reza Azad, Moein Heidari, Julien Cohen-Adad +2
Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related diseases such as osteoporosis, vertebral frac…
Contextual Attention Network: Transformer Meets U-Net
Reza Azad, Moein Heidari, Yuli Wu +1
Currently, convolutional neural networks (CNN) (e.g., U-Net) have become the de facto standard and attained immense success in medical image segmentation. However, as a downside, C…