AIMC Topic: Deep Learning

Clear Filters Showing 23021 to 23030 of 28423 articles

Direct Segmentation-Based Full Quantification for Left Ventricle via Deep Multi-Task Regression Learning Network.

IEEE journal of biomedical and health informatics
Quantitative analysis of the heart is extremely necessary and significant for detecting and diagnosing heart disease, yet there are still some challenges. In this study, we propose a new end-to-end segmentation-based deep multi-task regression learni...

Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Recurrence is the main risk for high-grade serous ovarian cancer (HGSOC) and few prognostic biomarkers were reported. In this study, we proposed a novel deep learning (DL) method to extract prognostic biomarkers from preoperat...

Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis.

Human brain mapping
In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive Impairment (MCI), from normal aging subjects. Multimodality neuroimagi...

A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data.

Accident; analysis and prevention
The primary objective of this study is to investigate how the deep learning approach contributes to citywide short-term crash risk prediction by leveraging multi-source datasets. This study uses data collected from Manhattan in New York City to illus...

A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition.

Sensors (Basel, Switzerland)
Recently, modern smartphones equipped with a variety of embedded-sensors, such as accelerometers and gyroscopes, have been used as an alternative platform for human activity recognition (HAR), since they are cost-effective, unobtrusive and they facil...

Multilayer Hybrid Deep-Learning Method for Waste Classification and Recycling.

Computational intelligence and neuroscience
This study proposes a multilayer hybrid deep-learning system (MHS) to automatically sort waste disposed of by individuals in the urban public area. This system deploys a high-resolution camera to capture waste image and sensors to detect other useful...

Large-Scale Multi-Class Image-Based Cell Classification With Deep Learning.

IEEE journal of biomedical and health informatics
Recent advances in ultra-high-throughput microscopy have enabled a new generation of cell classification methodologies using image-based cell phenotypes alone. In contrast to current single-cell analysis techniques that rely solely on slow and costly...

Simultaneous Cell Detection and Classification in Bone Marrow Histology Images.

IEEE journal of biomedical and health informatics
Recently, deep learning frameworks have been shown to be successful and efficient in processing digital histology images for various detection and classification tasks. Among these tasks, cell detection and classification are key steps in many comput...

Intra-Slice Motion Correction of Intravascular OCT Images Using Deep Features.

IEEE journal of biomedical and health informatics
Intra-slice motion correction is an important step for analyzing volume variations and pathological formations from intravascular imaging. Optical coherence tomography (OCT) has been recently introduced for intravascular imaging and assessment of cor...

Learning deep similarity metric for 3D MR-TRUS image registration.

International journal of computer assisted radiology and surgery
PURPOSE: The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image registration. H...