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Found 15 papers, 0 papers with code
Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas
The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function.
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.
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.
Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space
In this paper, we propose a method to automatically detect and correct motion-related artefacts in CMR acquisitions during reconstruction from k-space data.
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.
Quality-aware semi-supervised learning for CMR segmentation
SemiQCSeg can be an efficient approach for training segmentation networks for medical image data when labelled datasets are scarce.
Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients
To the best of our knowledge, this is the first attempt to measure spleen size in a fully automated way from ultrasound images.
Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation
The subject of "fairness" in artificial intelligence (AI) refers to assessing AI algorithms for potential bias based on demographic characteristics such as race and gender, and the development of algorithms to address this bias.
Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data
The framework consists of a deep learning model for the reconstruction of 2D+t cardiac cine MRI images from undersampled data, an image quality-control step to detect good quality reconstructions, followed by a deep learning model for bi-ventricular segmentation, a quality-control step to detect good quality segmentations and automated calculation of cardiac functional parameters.
The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images
Our dataset contains short axis images from 4 different MR scanners and 3 different pathology groups.
Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions
This work showcases, for the first time, the potential of DL in automating the process of effusion assessment from ultrasound in LMIC settings where there is often a lack of experienced radiologists to perform such tasks.
A systematic study of race and sex bias in CNN-based cardiac MR segmentation
We present the first systematic study of the impact of training set imbalance on race and sex bias in CNN-based segmentation.
Addressing Deep Learning Model Calibration Using Evidential Neural Networks and Uncertainty-Aware Training
However, in our complex artefact detection task, we saw an improvement in calibration for both a low and higher-capacity model when implementing both the ENN and uncertainty-aware training together, indicating that this approach can offer a promising way to improve calibration in such settings.
Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views
While spleen length measured from ultrasound images is a commonly used surrogate for spleen size, spleen volume remains the gold standard metric for assessing splenomegaly and the severity of related clinical conditions.
Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images
The best-performing model in terms of both classification accuracy and the most common calibration measure, expected calibration error (ECE) was the Confidence Weight method, a novel approach that weights the loss of samples to explicitly penalise confident incorrect predictions.
