97 citations · 215 across the 12 of their papers we have counts for
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DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation
Reza Azad, René Arimond, Ehsan Khodapanah Aghdam +2
Transformers have recently gained attention in the computer vision domain due to their ability to model long-range dependencies. However, the self-attention mechanism, which is the…
Medical Image Segmentation Review: The success of U-Net
Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland +7
Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most wi…
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…
Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation
Ehsan Khodapanah Aghdam, Reza Azad, Maral Zarvani +1
Melanoma is caused by the abnormal growth of melanocytes in human skin. Like other cancers, this life-threatening skin cancer can be treated with early diagnosis. To support a diag…
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…
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…