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Found 4 papers, 4 papers with code
Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution
Our attention module uses the convolution operation to perform joint spatial-channel attention on multiple concatenated input tensors, where the kernel (receptive field) size controls the reduction rate of the spatial attention, and the number of convolutional filters controls the reduction rate of the channel attention, respectively.
Ranked #1 on
Image Super-Resolution
on IXI
CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation
Our neural model can be trained on unpaired images, due to the integration of a multi-level cycle-consistency loss.
Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks
In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge.
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI Scans
We evaluate our method in the context of 2D and 3D super-resolution of CT and MRI scans from two databases, comparing it to relevant related works from the literature and baselines based on various interpolation schemes, using 2x and 4x scaling factors.
Ranked #6 on
Image Super-Resolution
on IXI
