AIMC Topic: Cloud Computing

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IMACEL: A cloud-based bioimage analysis platform for morphological analysis and image classification.

PloS one
Automated quantitative image analysis is essential for all fields of life science research. Although several software programs and algorithms have been developed for bioimage processing, an advanced knowledge of image processing techniques and high-p...

U-Net: deep learning for cell counting, detection, and morphometry.

Nature methods
U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their dat...

Cloud Deployment of High-Resolution Medical Image Analysis With TOMAAT.

IEEE journal of biomedical and health informatics
BACKGROUND: Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art, most of the methods cannot be easily accessed, compared or...

CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation.

Nature methods
As biomedical imaging datasets expand, deep neural networks are considered vital for image processing, yet community access is still limited by setting up complex computational environments and availability of high-performance computing resources. We...

An improved robust heteroscedastic probabilistic neural network based trust prediction approach for cloud service selection.

Neural networks : the official journal of the International Neural Network Society
Trustworthiness is a comprehensive quality metric which is used to assess the quality of the services in service-oriented environments. However, trust prediction of cloud services based on the multi-faceted Quality of Service (QoS) attributes is a ch...

Machine learning "red dot": open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification.

Clinical radiology
AIM: To develop a machine learning-based model for the binary classification of chest radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting prioritisation.

Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification.

Journal of biomedical informatics
INTRODUCTION: An approach to building a hybrid simulation of patient flow is introduced with a combination of data-driven methods for automation of model identification. The approach is described with a conceptual framework and basic methods for comb...

Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds.

Computers in biology and medicine
BACKGROUND: Muscle activation level is currently being captured using impractical and expensive devices which make their use in telemedicine settings extremely difficult. To address this issue, a prototype is presented of a non-invasive, easy-to-inst...

Providing Healthcare-as-a-Service Using Fuzzy Rule Based Big Data Analytics in Cloud Computing.

IEEE journal of biomedical and health informatics
With advancements in information and communication technology, there is a steep increase in the remote healthcare applications in which patients can get treatment from the remote places also. The data collected about the patients by remote healthcare...

Random ensemble learning for EEG classification.

Artificial intelligence in medicine
Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rap...