AIMC Topic: Deep Learning

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FusionAI, a DNA-sequence-based deep learning protocol reduces the false positives of human fusion gene prediction.

STAR protocols
Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes ...

A Deep Learning-Based Chinese Semantic Parser for the Almond Virtual Assistant.

Sensors (Basel, Switzerland)
Almond is an extendible open-source virtual assistant designed to help people access Internet services and IoT (Internet of Things) devices. Both are referred to as skills here. Service providers can easily enable their devices for Almond by defining...

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights.

Sensors (Basel, Switzerland)
COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in via...

Linear Regression vs. Deep Learning: A Simple Yet Effective Baseline for Human Body Measurement.

Sensors (Basel, Switzerland)
We propose a linear regression model for the estimation of human body measurements. The input to the model only consists of the information that a person can self-estimate, such as height and weight. We evaluate our model against the state-of-the-art...

Small intestinal viability assessment using dielectric relaxation spectroscopy and deep learning.

Scientific reports
Intestinal ischemia is a serious condition where the surgeon often has to make important but difficult decisions regarding resections and resection margins. Previous studies have shown that 3 h (hours) of warm full ischemia of the small bowel followe...

A CT image feature space (CTIS) loss for restoration with deep learning-based methods.

Physics in medicine and biology
Deep learning-based methods have been widely used in medical imaging field such as detection, segmentation and image restoration. For supervised learning methods in CT image restoration, different loss functions will lead to different image qualities...

Deep learning for biosignal control: insights from basic to real-time methods with recommendations.

Journal of neural engineering
Biosignal control is an interaction modality that allows users to interact with electronic devices by decoding the biological signals emanating from the movements or thoughts of the user. This manner of interaction with devices can enhance the sense ...

Objective quantification of nerves in immunohistochemistry specimens of thyroid cancer utilising deep learning.

PLoS computational biology
Accurate quantification of nerves in cancer specimens is important to understand cancer behaviour. Typically, nerves are manually detected and counted in digitised images of thin tissue sections from excised tumours using immunohistochemistry. Howeve...

Deep Learning-Based Diffusion-Weighted Magnetic Resonance Imaging in the Diagnosis of Ischemic Penumbra in Early Cerebral Infarction.

Contrast media & molecular imaging
The prefiltered image was imported into the local higher-order singular value decomposition (HOSVD) denoising algorithm (GL-HOSVD)-optimized diffusion-weighted imaging (DWI) image, which was compared with the deviation correction nonlocal mean (NL me...