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Found 11 papers, 3 papers with code
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies
Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans.
Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI
HP-ALF can perceive the image information in the hierarchical mechanism: image-level perception and patch-level perception.
Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network
However, most machine and deep learning based approaches are supervised and developed using a large number of training samples, in which the pixelwise annotations are expensive and sometimes can be impossible to obtain.
Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI
The main obstacle is the computational cost of the self-attention layer, which is the core part of the Transformer, can be expensive for high resolution MRI inputs.
CS$^2$: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention
The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance.
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers
Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e. g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning.
Swin Transformer for Fast MRI
The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers.
Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstruction
To improve image quality from sparse-view data, a Multi-domain Integrative Swin Transformer network (MIST-net) was developed in this article.
Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention
Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0. 27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices).
Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation
Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success.
Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm
Accurate detection of the myocardial infarction (MI) area is crucial for early diagnosis planning and follow-up management.
