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Found 21 papers, 6 papers with code
Block Attention and Switchable Normalization Based Deep Learning Framework for Segmentation of Retinal Vessels
The presence of high blood sugar levels damages blood vessels and causes an eye condition called diabetic retinopathy.
Explainable Image Quality Assessment for Medical Imaging
We apply a variety of techniques to measure the faithfulness of the saliency detectors, and our explainable pipeline relies on NormGrad, an algorithm which can efficiently localise image quality issues with saliency maps of the classifier.
Semi-Supervised Segmentation of Multi-vendor and Multi-center Cardiac MRI using Histogram Matching
In this paper, we propose a semi-supervised segmentation setup for leveraging unlabeled data to segment Left-ventricle, Right-ventricle, and Myocardium.
Prostate Lesion Estimation using Prostate Masks from Biparametric MRI
Biparametric MRI has emerged as an alternative to multiparametric prostate MRI, which eliminates the need for the potential harms to the patient due to the contrast medium.
Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation
In cardiovascular magnetic resonance imaging (CMR), respiratory motion represents a major challenge in terms of acquisition quality and therefore subsequent analysis and final diagnosis.
Shifted Windows Transformers for Medical Image Quality Assessment
While we obtain a classification accuracy of 87. 1% and 95. 48% on the Object-CXR and LVOT datasets, our experimental results suggest that the use of Swin Transformer improves the Object-CXR classification performance while obtaining a comparable performance for the LVOT dataset.
A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis
Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation.
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment.
A survey on shape-constraint deep learning for medical image segmentation
Since the advent of U-Net, fully convolutional deep neural networks and its many variants have completely changed the modern landscape of deep learning based medical image segmentation.
Channel Attention Networks for Robust MR Fingerprinting Matching
Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times.
Transfer Learning for Electricity Price Forecasting
The accurate prediction of the day-ahead electricity prices is an active research field and available data from various markets can be used as an input for forecasting.
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.
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.
Magnetic Resonance Fingerprinting using Recurrent Neural Networks
Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time-efficient acquisition.
Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning
Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space.
Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection
As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem.
