150 research outputs found
EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks
Ejection fraction (EF) is a key indicator of cardiac function, allowing
identification of patients prone to heart dysfunctions such as heart failure.
EF is estimated from cardiac ultrasound videos known as echocardiograms (echo)
by manually tracing the left ventricle and estimating its volume on certain
frames. These estimations exhibit high inter-observer variability due to the
manual process and varying video quality. Such sources of inaccuracy and the
need for rapid assessment necessitate reliable and explainable machine learning
techniques. In this work, we introduce EchoGNN, a model based on graph neural
networks (GNNs) to estimate EF from echo videos. Our model first infers a
latent echo-graph from the frames of one or multiple echo cine series. It then
estimates weights over nodes and edges of this graph, indicating the importance
of individual frames that aid EF estimation. A GNN regressor uses this weighted
graph to predict EF. We show, qualitatively and quantitatively, that the
learned graph weights provide explainability through identification of critical
frames for EF estimation, which can be used to determine when human
intervention is required. On EchoNet-Dynamic public EF dataset, EchoGNN
achieves EF prediction performance that is on par with state of the art and
provides explainability, which is crucial given the high inter-observer
variability inherent in this task.Comment: Published in MICCAI 202
Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise
Deep neural networks have proven to be highly effective when large amounts of
data with clean labels are available. However, their performance degrades when
training data contains noisy labels, leading to poor generalization on the test
set. Real-world datasets contain noisy label samples that either have similar
visual semantics to other classes (in-distribution) or have no semantic
relevance to any class (out-of-distribution) in the dataset. Most
state-of-the-art methods leverage ID labeled noisy samples as unlabeled data
for semi-supervised learning, but OOD labeled noisy samples cannot be used in
this way because they do not belong to any class within the dataset. Hence, in
this paper, we propose incorporating the information from all the training data
by leveraging the benefits of self-supervised training. Our method aims to
extract a meaningful and generalizable embedding space for each sample
regardless of its label. Then, we employ a simple yet effective K-nearest
neighbor method to remove portions of out-of-distribution samples. By
discarding these samples, we propose an iterative "Manifold DivideMix"
algorithm to find clean and noisy samples, and train our model in a
semi-supervised way. In addition, we propose "MixEMatch", a new algorithm for
the semi-supervised step that involves mixup augmentation at the input and
final hidden representations of the model. This will extract better
representations by interpolating both in the input and manifold spaces.
Extensive experiments on multiple synthetic-noise image benchmarks and
real-world web-crawled datasets demonstrate the effectiveness of our proposed
framework. Code is available at https://github.com/Fahim-F/ManifoldDivideMix
Cross-sectional area calculation for arbitrary shape in the image using star algorithm with Green's theorem
Calculation of the cross-sectional area is an important diagnostic tool in medical imaging modality. Curvature points arrangement (CPA) is an important step in the calculation, where the Star algorithm had been shown to be effective in segmenting the carotid artery. The algorithm however works under the assumption of circular or ellipsoid shapes, and the ability to determine its center of gravity is done by exploiting the features of equi-space diameter of the circle. In this paper, a method of calculation of the cross-sectional area of an arbitrary shape is discussed. The Star algorithm is modified to arrange the points of the object's edge through the CPA process in order to form a simple closed curve. Several rays are emanated from a point inside the region of interest with different angles to the far points within the segmented area. The cross-sectional area is then calculated by using Green's theorem. To validate the concepts, several regular shaped images with different noise types (Gaussian, speckles, and salt and pepper) and ultrasound images are used in the experiments. The result shows that this method can calculate the cross-sectional area with negligible error for an arbitrary object within the image and with different types of noises
Fusion analysis of functional MRI data for classification of individuals based on patterns of activation.
Classification of individuals based on patterns of brain activity observed in functional MRI contrasts may be helpful for diagnosis of neurological disorders. Prior work for classification based on these patterns have primarily focused on using a single contrast, which does not take advantage of complementary information that may be available in multiple contrasts. Where multiple contrasts are used, the objective has been only to identify the joint, distinct brain activity patterns that differ between groups of subjects; not to use the information to classify individuals. Here, we use joint Independent Component Analysis (jICA) within a Support Vector Machine (SVM) classification method, and take advantage of the relative contribution of activation patterns generated from multiple fMRI contrasts to improve classification accuracy. Young (age: 19-26) and older (age: 57-73) adults (16 each) were scanned while listening to noise alone and to speech degraded with noise, half of which contained meaningful context that could be used to enhance intelligibility. Functional contrasts based on these conditions (and a silent baseline condition) were used within jICA to generate spatially independent joint activation sources and their corresponding modulation profiles. Modulation profiles were used within a non-linear SVM framework to classify individuals as young or older. Results demonstrate that a combination of activation maps across the multiple contrasts yielded an area under ROC curve of 0.86, superior to classification resulting from individual contrasts. Moreover, class separability, measured by a divergence criterion, was substantially higher when using the combination of activation maps
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