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Found 10 papers, 1 papers with code
DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation
Unlike earlier transformer-based U-net models, DA-TransUNet utilizes Transformers and DA-Block to integrate not only global and local features, but also image-specific positional and channel features, improving the performance of medical image segmentation.
A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions
In this work, we propose a novel peer-to-peer federated continual learning strategy to improve low-dose CT imaging performance from multiple institutions.
Lesion classification by model-based feature extraction: A differential affine invariant model of soft tissue elasticity
The outcomes of this modeling approach reached the score of area under the curve of the receiver operating characteristics of 94. 2 % for the polyps and 87. 4 % for the nodules, resulting in an average gain of 5 % to 30 % over ten existing state-of-the-art lesion classification methods.
Improving Smart Conference Participation through Socially-Aware Recommendation
This research addresses recommending presentation sessions at smart conferences to participants.
Towards Multiple Enhancement Styles Generation in Mammography
The presented DL framework is denoted as DL-ME for simplicity.
Direct Energy-resolving CT Imaging via Energy-integrating CT images using a Unified Generative Adversarial Network
In this study, the present uGAN generates ErCT images at 70keV, 90keV, 110keV, and 130keV simultaneously from EiCT images at140kVp.
Generative Adversarial Network
Image-to-Image Translation
+1
Machine learning electron correlation in a disordered medium
Learning from data has led to a paradigm shift in computational materials science.
Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features
The fusion algorithm takes full advantage of the handcrafted features and the highest level CNN features learned at the output layer.
Radon Inversion via Deep Learning
Qualitative results show promising reconstruction performance of the iRadonMap.
Statistical models and regularization strategies in statistical image reconstruction of low-dose X-ray CT: a survey
Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose X-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method for various clinical tasks.
