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Chapter and Conference Paper
Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
Over the past decade, deep convolutional neural networks have been widely adopted for medical image segmentation and shown to achieve adequate performance. However, due to inherent inductive biases present in ...
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Chapter and Conference Paper
Over-and-Under Complete Convolutional RNN for MRI Reconstruction
Reconstructing magnetic resonance (MR) images from under-sampled data is a challenging problem due to various artifacts introduced by the under-sampling operation. Recent deep learning-based methods for MR ima...
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Chapter and Conference Paper
KiU-Net: Towards Accurate Segmentation of Biomedical Images Using Over-Complete Representations
Due to its excellent performance, U-Net is the most widely used backbone architecture for biomedical image segmentation in the recent years. However, in our studies, we observe that there is a considerable per...