Researchers from Peking University Third Hospital have developed a novel collaborative framework that integrates various semi-supervised learning techniques to enhance MRI segmentation using unlabeled ...
Through a comprehensive evaluation of model complexity and number of parameters, it was determined that the overall performance of the proposed model is the best when eight group convolutions are used ...
Researchers from Peking University Third Hospital have developed a novel collaborative framework that integrates various semi-supervised learning techniques to enhance MRI segmentation using unlabeled ...
T1-weighted MRIs have been chosen for our study due to the difficulties in achieving a better clustering for brain tissue regions. The clustering segmentation method processed MRIs of brain tissue ...
Primary lung cancer remains the leading cause of cancer-related death in the Western world, and the lung is a common site for recurrence of extrathoracic malignancies. Small-animal (rodent) models of ...
Drawing inspiration from the popular VGG networks, the paper proposes using a deep convolutional neural network architecture with small convolutional kernels for segmentation of gliomas in MRI images.
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