97 citations · 182 across the 5 of their papers we have counts for
12 papers
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…
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…
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities
Reza Azad, Nika Khosravi, Dorit Merhof
Gliomas are one of the most prevalent types of primary brain tumours, accounting for more than 30\% of all cases and they develop from the glial stem or progenitor cells. In theory…
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…
Medical Image Segmentation on MRI Images with Missing Modalities: A Review
Reza Azad, Nika Khosravi, Mohammad Dehghanmanshadi +2
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming their negative repercussions is considered a hurdle in biomedical imaging. The combination of a s…