Results 11 to 20 of about 206,029 (232)
STHarDNet: Swin Transformer with HarDNet for MRI Segmentation
Applied Sciences (Switzerland), 2022 In magnetic resonance imaging (MRI) segmentation, conventional approaches utilize U-Net models with encoder–decoder structures, segmentation models using vision transformers, or models that combine a vision transformer with an encoder–decoder model ...
Zhegao Piao
exaly +4 more sources
Asymmetric convolution Swin transformer for medical image super-resolution
AEJ - Alexandria Engineering Journal, 2023 Medical Image Super-Resolution plays a pivotal role in enhancing diagnostic accuracy. Transformer-based methods, such as Image Restoration Using Swin Transformer (SwinIR) and Swin transformer for fast Magnetic Resonance Imaging (SwinMR), have shown ...
Jiehui Jiang
exaly +4 more sources
SwiFT: Swin 4D fMRI Transformer [PDF]
Advances in Neural Information Processing Systems 36, 2023 Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans.
Peter Yongho Kim +8 more
core +7 more sources
Swin-HSSAM: A green coffee bean grading method by Swin transformer.
PLoS ONEA novel shifted window (Swin) Transformer coffee bean grading model called Swin-HSSAM has been proposed to address the challenges of accurately classifying green coffee beans and low identification accuracy.
Yujie Jiao +9 more
doaj +4 more sources
Sparseswin: Swin Transformer with Sparse Transformer Block [PDF]
Neurocomputing, 2023 Advancements in computer vision research have put transformer architecture as the state of the art in computer vision tasks. One of the known drawbacks of the transformer architecture is the high number of parameters, this can lead to a more complex and inefficient algorithm.
Krisna Pinasthika +4 more
openaire +5 more sources
Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
Applied Sciences, 2022 Transformer complements convolutional neural network (CNN) has achieved better performance than improved CNN-based methods. Specially, Transformer is utilized to be combined with U-shaped structure, skip-connections, encoder, and even them all together ...
Zhihao Liao, Neng Fan, Kai Xu
doaj +2 more sources
Sq-Swin: Siamese Quadratic Swin Transformer for Lettuce Browning Prediction
IEEE Access, 2023 Enzymatic browning is a major quality defect of packaged “ready-to-eat” fresh-cut lettuce salads. While there have been many research and breeding efforts to counter this problem, progress is hindered by the lack of a technology to identify and quantify browning rapidly, objectively, and reliably. Here, we report a deep learning model for
Dayang Wang +4 more
openaire +4 more sources
S-Swin Transformer: simplified Swin Transformer model for offline handwritten Chinese character recognition [PDF]
PeerJ Computer Science, 2022 The Transformer shows good prospects in computer vision. However, the Swin Transformer model has the disadvantage of a large number of parameters and high computational effort.
Yongping Dan +3 more
doaj +5 more sources
HEAL-SWIN: A Vision Transformer on the Sphere [PDF]
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Accepted as poster to CVPR 2024. Main body: 10 pages, 7 figures.
Oscar Carlsson +6 more
core +5 more sources
SQ-Swin: a Pretrained Siamese Quadratic Swin Transformer for Lettuce Browning Prediction [PDF]
CoRR, 2022 Packaged fresh-cut lettuce is widely consumed as a major component of vegetable salad owing to its high nutrition, freshness, and convenience. However, enzymatic browning discoloration on lettuce cut edges significantly reduces product quality and shelf life. While there are many research and breeding efforts underway to minimize browning, the progress
Dayang Wang +4 more
openaire +3 more sources