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Data Compression

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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...

Human mitochondrial genome compression using machine learning techniques.

Human genomics
BACKGROUND: In recent years, with the development of high-throughput genome sequencing technologies, a large amount of genome data has been generated, which has caused widespread concern about data storage and transmission costs. However, how to effe...

Evolutionary Compression of Deep Neural Networks for Biomedical Image Segmentation.

IEEE transactions on neural networks and learning systems
Biomedical image segmentation is lately dominated by deep neural networks (DNNs) due to their surpassing expert-level performance. However, the existing DNN models for biomedical image segmentation are generally highly parameterized, which severely i...

A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone.

Sensors (Basel, Switzerland)
As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans' daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive gro...

Region-of-interest undersampled MRI reconstruction: A deep convolutional neural network approach.

Magnetic resonance imaging
Compressive sensing enables fast magnetic resonance imaging (MRI) reconstruction with undersampled k-space data. However, in most existing MRI reconstruction models, the whole MR image is targeted and reconstructed without taking specific tissue regi...

Learning the implicit strain reconstruction in ultrasound elastography using privileged information.

Medical image analysis
Quasi-static ultrasound elastography is an importance imaging technology to assess the conditions of various diseases through reconstructing the tissue strain from radio frequency data. State-of-the-art strain reconstruction techniques suffer from th...

A Deep Information Sharing Network for Multi-Contrast Compressed Sensing MRI Reconstruction.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Compressed sensing (CS) theory can accelerate multi-contrast magnetic resonance imaging (MRI) by sampling fewer measurements within each contrast. However, conventional optimization-based reconstruction models suffer several limitations, including a ...

Investigating the transferring capability of capsule networks for text classification.

Neural networks : the official journal of the International Neural Network Society
Text classification has been attracting increasing attention with the growth of textual data created on the Internet. Great progress has been made by deep neural networks for domains where a large amount of labeled training data is available. However...

A new approach for arrhythmia classification using deep coded features and LSTM networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor hav...

A novel ECG signal compression method using spindle convolutional auto-encoder.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: With rapid development of telehealth system and cloud platform, traditional 12-ECG signals with high resolution generate heavy burdens in data storage and transmission. This problem is increasingly addressed with various EC...