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Two-Stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic Resonance
Segmenting cardiac scars and edema from cardiac magnetic resonance (CMR) are essential for the early diagnosis and accurate prognostic assessment of...
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Histogram Matching Augmentation for Domain Adaptation with Application to Multi-centre, Multi-vendor and Multi-disease Cardiac Image Segmentation
Convolutional Neural Networks (CNNs) have achieved high accuracy for cardiac structure segmentation if training cases and testing cases are from the...
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A deformable convolutional time-series prediction network with extreme peak and interval calibration
Deep modeling and analysis of human big data deepens our understanding of human activities. Periodic time-series signals, e.g., electrocardiographs,...
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Deidentifying MRI Data Domain by Iterative Backpropagation
Medical images acquired at various hospitals can differ significantly in their data distribution. We can find multiple sources of divergence...
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Ensemble of Deep Convolutional Neural Networks with Monte Carlo Dropout Sampling for Automated Image Segmentation Quality Control and Robust Deep Learning Using Small Datasets
Recent progress on deep learning (DL)-based medical image segmentation can enable fast extraction of clinical parameters for efficient clinical...
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A novel multi-task semi-supervised medical image segmentation method based on multi-branch cross pseudo supervision
Medical image segmentation is a crucial task in many clinical applications, such as tumor detection and surgical planning. However, the annotation...
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Mass Univariate Regression Analysis for Three-Dimensional Liver Image-Derived Phenotypes
Image-derived phenotypes of abdominal organs from magnetic resonance imaging reveal variations in volume and shape and may be used to model changes...
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Methodology and real-world applications of dynamic uncertain causality graph for clinical diagnosis with explainability and invariance
AI-aided clinical diagnosis is desired in medical care. Existing deep learning models lack explainability and mainly focus on image analysis. The...
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Shape Constrained CNN for Cardiac MR Segmentation with Simultaneous Prediction of Shape and Pose Parameters
Semantic segmentation using convolutional neural networks (CNNs) is the state-of-the-art for many medical segmentation tasks including left ventricle...
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Information Quality of Reddit Link Posts on Health News
Inaccuracy has been a common problem in news coverage of scientific research. This problem has been particularly prevalent in health research news....
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Multi-modal multi-head self-attention for medical VQA
Medical Visual Question answering (MedVQA) systems provide answers to questions based on radiology images. Medical images are more complex than...
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A Threat Towards the Neonatal Mortality
This paper focusses on review and an analysis of the observational studies or case control studies for identification of the threats to the neonatal...
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CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study
The Coronavirus disease 2019 (COVID-19) is raging across the world. The radiomics, which explores huge amounts of features from medical image for...
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Integration of AI for Clinical Decision Support
Clinical decision support provides clinicians and patients with medical knowledge, information, and inferences that are designed to...
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Patient–Physician Relationship in Telemedicine
The first therapy that the physician administers to the patient is the physician himself. This ensures that the physician–patient relationship is the...
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DNetUnet: a semi-supervised CNN of medical image segmentation for super-computing AI service
Deep learning approaches have achieved good performance in segmenting medical images. In this paper, we propose a new convolutional neural network...
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Densely Connected Fully Convolutional Network for Short-Axis Cardiac Cine MR Image Segmentation and Heart Diagnosis Using Random Forest
In this paper, we propose a fully automatic method for segmentation of left ventricle, right ventricle and myocardium from cardiac Magnetic Resonance...
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Conditional Generative Adversarial Networks for the Prediction of Cardiac Contraction from Individual Frames
Cardiac anatomy and function are interrelated in many ways, and these relations can be affected by multiple pathologies. In particular, this applies...
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AI and IoT in Healthcare
The web of things has various applications in therapeutic organizations, from remote checking to sharp sensors and medicinal gadget blend. It can...
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Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images
Segmentation of the heart in cardiac cine MR is clinically used to quantify cardiac function. We propose a fully automatic method for segmentation...