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Found 15 papers, 4 papers with code
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification
Early detection of myocardial infarction (MI), a critical condition arising from coronary artery disease (CAD), is vital to prevent further myocardial damage.
SAF-Net: Self-Attention Fusion Network for Myocardial Infarction Detection using Multi-View Echocardiography
Myocardial infarction (MI) is a severe case of coronary artery disease (CAD) and ultimately, its detection is substantial to prevent progressive damage to the myocardium.
MAMAF-Net: Motion-Aware and Multi-Attention Fusion Network for Stroke Diagnosis
Stroke is a major cause of mortality and disability worldwide from which one in four people are in danger of incurring in their lifetime.
R2C-GAN: Restore-to-Classify GANs for Blind X-Ray Restoration and COVID-19 Classification
The proposed restoration approach achieves over 90% F1-Score which is significantly higher than the performance of any deep model.
Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography
In this study, we propose a framework for early detection of MI over multi-view echocardiography that leverages one-class classification (OCC) techniques.
OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images
To address the data scarcity encountered in training and especially in evaluation, this study extends the largest COVID-19 CXR dataset: QaTa-COV19 with 121, 378 CXRs including 9258 COVID-19 samples with their corresponding ground-truth segmentation masks that are publicly shared with the research community.
Early Myocardial Infarction Detection over Multi-view Echocardiography
Following the blockage of a coronary artery, the regional wall motion abnormality (RWMA) of the ischemic myocardial segments is the earliest change to set in.
Reliable COVID-19 Detection Using Chest X-ray Images
To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects.
Early Detection of Myocardial Infarction in Low-Quality Echocardiography
Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening health problem worldwide from which 32. 4 million people suffer each year.
COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
To accomplish this, we have compiled the largest dataset with 119, 316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach.
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection
It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their "maximum motion displacement" plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF).
Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage.
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Any technological tool that can be provided to healthcare practitioners to save time, effort, and possibly lives has crucial importance.
Multilinear Compressive Learning
Compressive Learning is an emerging topic that combines signal acquisition via compressive sensing and machine learning to perform inference tasks directly on a small number of measurements.
Compressively Sensed Image Recognition
Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal.
