Convolutional neural networks (CNNs) have been the consensus for medical image
segmentation tasks. However, they inevitably suffer from the limitation in modeling long …

Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming
their negative repercussions is considered a hurdle in biomedical imaging. The combination …

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 …

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 …

Convolutional neural networks (CNN)(eg, UNet) have become the de facto standard and
attained immense success in medical image segmentation. However, CNN based methods …

Anatomical consistency in biomarker segmentation is crucial for many medical image
analysis tasks. A promising paradigm for achieving anatomically consistent segmentation …

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 U-Net model …

Gliomas are one of the most prevalent types of primary brain tumors, accounting for more
than 30% of all cases and they develop from the glial stem or progenitor cells. In theory, the …

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 …

Melanoma is considered one of the most dangerous skin cancer diseases that threaten
human health and life. Early diagnosis of melanoma is a big challenge, especially with the …