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Found 14 papers, 1 papers with code
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.
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.
Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data
For the inter-domain learning, a consistency constraint is applied to the LAs modelled by two dual-modelling networks to exploit the complementary knowledge among different data domains.
JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets
In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way.
Annealing Genetic GAN for Minority Oversampling
In particular, the generator uses different training strategies to generate multiple offspring and retain the best.
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness
The performance of traditional compressive sensing-based MRI (CS-MRI) reconstruction is affected by its slow iterative procedure and noise-induced artefacts.
MV-RAN: Multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis
In addition, for a better interpretation of pathophysiological processes, clinical decision-making and prognosis, such cardiac anatomy segmentation and quantitative analysis of various clinical indices should ideally be performed for the data covering the full cardiac cycle.
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).
Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
Our method is built as an end-to-end framework for segmentation and classification.
Discriminative Consistent Domain Generation for Semi-supervised Learning
Based on the generated discriminative consistent domain, we can use the unlabeled data to learn the task model along with the labeled data via a consistent image generation.
Direct Quantification for Coronary Artery Stenosis Using Multiview Learning
The proposed DMQCA model consists of a multiview module with two attention mechanisms, a key-frame module, and a regression module, to achieve direct accurate multiple-index estimation.
FIRE: Unsupervised bi-directional inter-modality registration using deep networks
Inter-modality image registration is an critical preprocessing step for many applications within the routine clinical pathway.
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.
