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Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiac perfusion magnetic resonance imaging (MRI) with first pass dynamic contrast enhancement (DCE) is a useful tool to identify perfusion defects in myocardial tissues. Automatic segmentation of the myocardium can lead to...

Breast cancer histopathology image classification through assembling multiple compact CNNs.

BMC medical informatics and decision making
BACKGROUND: Breast cancer causes hundreds of thousands of deaths each year worldwide. The early stage diagnosis and treatment can significantly reduce the mortality rate. However, the traditional manual diagnosis needs intense workload, and diagnosti...

Decentralized distribution-sampled classification models with application to brain imaging.

Journal of neuroscience methods
BACKGROUND: In this age of big data, certain models require very large data stores in order to be informative and accurate. In many cases however, the data are stored in separate locations requiring data transfer between local sites which can cause v...

Pulse-Wave-Pattern Classification with a Convolutional Neural Network.

Scientific reports
Owing to the diversity of pulse-wave morphology, pulse-based diagnosis is difficult, especially pulse-wave-pattern classification (PWPC). A powerful method for PWPC is a convolutional neural network (CNN). It outperforms conventional methods in patte...

Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning.

Medical image analysis
CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-d...

Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation.

Medical hypotheses
Liver and hepatic tumor segmentation remains a challenging problem in Computer Tomography (CT) images analysis due to its shape variation and vague boundary. The general hypothesis says that deep learning methods produce improved results on medical i...

Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics.

NeuroImage
There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there...

Deep representation learning for individualized treatment effect estimation using electronic health records.

Journal of biomedical informatics
Utilizing clinical observational data to estimate individualized treatment effects (ITE) is a challenging task, as confounding inevitably exists in clinical data. Most of the existing models for ITE estimation tackle this problem by creating unbiased...

Multi-task recurrent convolutional network with correlation loss for surgical video analysis.

Medical image analysis
Surgical tool presence detection and surgical phase recognition are two fundamental yet challenging tasks in surgical video analysis as well as very essential components in various applications in modern operating rooms. While these two analysis task...

Convolutional neural networks for multi-class brain disease detection using MRI images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The brain disorders may cause loss of some critical functions such as thinking, speech, and movement. So, the early detection of brain diseases may help to get the timely best treatment. One of the conventional methods used to diagnose these disorder...