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Found 13 papers, 2 papers with code
A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI
With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter observer variation.
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction
Our key contribution is that the VAE disentangles the latent space based on `explanations' drawn from existing clinical knowledge.
Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation
In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity.
A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology
We show that the incorporation of the prior knowledge of the topology of this anatomy improves the resulting segmentations in terms of both the topological accuracy and the Dice coefficient.
dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited.
Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging
The results show that the proposed pipeline can accurately estimate the respiratory state and reconstruct 4D SR volumes with better or similar performance to the 3D SVR pipeline with less than 20\% sparsely selected slices.
Topology-preserving augmentation for CNN-based segmentation of congenital heart defects from 3D paediatric CMR
In a series of five-fold cross-validations, we demonstrate the performance gain produced by this pipeline and the relevance of topological considerations to the segmentation of congenital heart defects.
Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders
Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health.
Global and Local Interpretability for Cardiac MRI Classification
Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their applicability in clinical practice.
Mechanically Powered Motion Imaging Phantoms: Proof of Concept
Motion imaging phantoms are expensive, bulky and difficult to transport and set-up.
Explicit topological priors for deep-learning based image segmentation using persistent homology
We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so.
Hybed: Hyperbolic Neural Graph Embedding
Neural embeddings have been used with great success in Natural Language Processing (NLP) where they provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks.
Transitive Reduction of Citation Networks
In many complex networks the vertices are ordered in time, and edges represent causal connections.
Physics and Society Digital Libraries Social and Information Networks
