AIMC Topic: Deep Learning

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Research on Multicamera Photography Image Art in BERT Motion Based on Deep Learning Mode.

Computational intelligence and neuroscience
In order to improve the artistic expression effect of photographic images, this article combines the deep learning model to conduct multicamera photographic image art research in BERT motion. Moreover, this article analyzes the external parameter err...

Improved diagnostic performance of plain radiography for cervical ossification of the posterior longitudinal ligament using deep learning.

PloS one
BACKGROUND: A high false-negative rate has been reported for the diagnosis of ossification of the posterior longitudinal ligament (OPLL) using plain radiography. We investigated whether deep learning (DL) can improve the diagnostic performance of rad...

Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans.

Radiology
Background After severe traumatic brain injury (sTBI), physicians use long-term prognostication to guide acute clinical care yet struggle to predict outcomes in comatose patients. Purpose To develop and evaluate a prognostic model combining deep lear...

Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma.

European journal of nuclear medicine and molecular imaging
PURPOSE: How to discriminate different risks of recurrent nasopharyngeal carcinoma (rNPC) patients and guide individual treatment has become of great importance. This study aimed to explore the associations between deep learning signatures and biolog...

Research on Vehicle Lane Change Warning Method Based on Deep Learning Image Processing.

Sensors (Basel, Switzerland)
In order to improve vehicle driving safety in a low-cost manner, we used a monocular camera to study a lane-changing warning algorithm for highway vehicles based on deep learning image processing technology. We improved the mask region-based convolut...

A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography.

Sensors (Basel, Switzerland)
Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-qualit...

Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches.

Genome medicine
The taxonomic structure of microbial community sample is highly habitat-specific, making source tracking possible, allowing identification of the niches where samples originate. However, current methods face challenges when source tracking is scaled ...

Research Progress of Machine Learning and Deep Learning in Intelligent Diagnosis of the Coronary Atherosclerotic Heart Disease.

Computational and mathematical methods in medicine
The coronary atherosclerotic heart disease is a common cardiovascular disease with high morbidity, disability, and societal burden. Early, precise, and comprehensive diagnosis of the coronary atherosclerotic heart disease is of great significance. Th...

Research on Rice Yield Prediction Model Based on Deep Learning.

Computational intelligence and neuroscience
Food is the paramount necessity of the people. With the progress of society and the improvement of social welfare system, the living standards of people all over the world are constantly improving. The development of medical industry improves people'...