AIMC Topic: Deep Learning

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An imConvNet-based deep learning model for Chinese medical named entity recognition.

BMC medical informatics and decision making
BACKGROUND: With the development of current medical technology, information management becomes perfect in the medical field. Medical big data analysis is based on a large amount of medical and health data stored in the electronic medical system, such...

Pterygium Screening and Lesion Area Segmentation Based on Deep Learning.

Journal of healthcare engineering
A two-category model and a segmentation model of pterygium were proposed to assist ophthalmologists in establishing the diagnosis of ophthalmic diseases. A total of 367 normal anterior segment images and 367 pterygium anterior segment images were col...

Real-time noise cancellation with deep learning.

PloS one
Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so ...

A Deep Learning Approach to Detect Anomalies in an Electric Power Steering System.

Sensors (Basel, Switzerland)
As anomaly detection for electrical power steering (EPS) systems has been centralized using model- and knowledge-based approaches, EPS system have become complex and more sophisticated, thereby requiring enhanced reliability and safety. Since most cu...

Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry.

Digestive diseases and sciences
BACKGROUND: We developed a deep learning algorithm to evaluate defecatory patterns to identify dyssynergic defecation using 3-dimensional high definition anal manometry (3D-HDAM).

Deep learning for hetero-homo conversion in channel-domain for phase aberration correction in ultrasound imaging.

Ultrasonics
Echo imaging in ultrasound computed tomography (USCT) using the synthetic aperture technique is performed with the assumption that the speed of sound is constant in the system. However, tissue heterogeneity causes a mismatch between the predicted arr...

Multi-center, multi-vendor validation of deep learning-based attenuation correction in SPECT MPI: data from the international flurpiridaz-301 trial.

European journal of nuclear medicine and molecular imaging
PURPOSE: Although SPECT myocardial perfusion imaging (MPI) is susceptible to artifacts from soft tissue attenuation, most scans are performed without attenuation correction. Deep learning-based attenuation corrected (DLAC) polar maps improved diagnos...

Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study.

Cancer
BACKGROUND: Neoadjuvant chemotherapy (NAC) can downstage tumors and axillary lymph nodes in breast cancer (BC) patients. However, tumors and axillary response to NAC are not parallel and vary among patients. This study aims to explore the feasibility...

Deep learning-based synapse counting and synaptic ultrastructure analysis of electron microscopy images.

Journal of neuroscience methods
BACKGROUND: Synapses are the connections between neurons in the central nervous system (CNS) or between neurons and other excitable cells in the peripheral nervous system (PNS), where electrical or chemical signals rapidly travel through one cell to ...

Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm.

International journal of environmental research and public health
Few studies classified and predicted hypertension using blood pressure (BP)-related determinants in a deep learning algorithm. The objective of this study is to develop a deep learning algorithm for the classification and prediction of hypertension w...