AIMC Topic: Deep Learning

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Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition.

Journal of biomedical informatics
Clinical named entity recognition aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translational research...

Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy.

International ophthalmology
PURPOSE: We investigated using ultrawide-field fundus images with a deep convolutional neural network (DCNN), which is a machine learning technology, to detect treatment-naïve proliferative diabetic retinopathy (PDR).

Deep learning for waveform identification of resting needle electromyography signals.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Given the recent advent in machine learning and artificial intelligence on medical data analysis, we hypothesized that the deep learning algorithm can classify resting needle electromyography (n-EMG) discharges.

Automatic inference of BI-RADS final assessment categories from narrative mammography report findings.

Journal of biomedical informatics
We propose an efficient natural language processing approach for inferring the BI-RADS final assessment categories by analyzing only the mammogram findings reported by the mammographer in narrative form. The proposed hybrid method integrates semantic...

BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information.

International journal of molecular sciences
The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental m...

Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography.

IEEE transactions on medical imaging
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far...

Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network.

Medical image analysis
As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures. These images can help reveal disease-related alterations below the surface of the retina, such as the presence o...

Evaluation of Cross-Validation Strategies in Sequence-Based Binding Prediction Using Deep Learning.

Journal of chemical information and modeling
Binding prediction between targets and drug-like compounds through deep neural networks has generated promising results in recent years, outperforming traditional machine learning-based methods. However, the generalization capability of these classif...

Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified dermatologists. However, the non-availability of a public human benchmark restricts the comp...

Reagent-Free and Rapid Assessment of T Cell Activation State Using Diffraction Phase Microscopy and Deep Learning.

Analytical chemistry
CD8 T cells constitute an essential compartment of the adaptive immune system. During immune responses, naı̈ve T cells become functional, as they are primed with their cognate determinants by the antigen presenting cells. Current methods of identifyi...