Search Results for author:
Found 9 papers, 5 papers with code
LoMAE: Low-level Vision Masked Autoencoders for Low-dose CT Denoising
Recently, transformer models emerged as a promising avenue to enhance LDCT image quality.
DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps Segmentation
Polyps segmentation poses a significant challenge in medical imaging due to the flat surface of polyps and their texture similarity to surrounding tissues.
Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model
In this work, we formulate a novel transformer (Gformer) based iterative modelling approach for the synthesis of images with arbitrary contrast enhancement that corresponds to different dose levels.
Masked Autoencoders for Low dose CT denoising
A plethora of transformer models have been developed recently to improve LDCT image quality.
SQ-Swin: a Pretrained Siamese Quadratic Swin Transformer for Lettuce Browning Prediction
To the best of our knowledge, it is the first-of-its-kind on deep learning for lettuce browning prediction using a pretrained Siamese Quadratic Swin (SQ-Swin) transformer with several highlights.
CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising
Furthermore, an overlapped inference mechanism is introduced to effectively eliminate the boundary artifacts that are common for encoder-decoder-based denoising models.
Manifoldron: Direct Space Partition via Manifold Discovery
A neural network with the widely-used ReLU activation has been shown to partition the sample space into many convex polytopes for prediction.
TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising
The model is free of convolution blocks and consists of a symmetric encoder-decoder block with sole transformer.
On a Sparse Shortcut Topology of Artificial Neural Networks
In this paper, we investigate the expressivity and generalizability of a novel sparse shortcut topology.
