AIMC Topic: Deep Learning

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Measurement-oriented deep-learning workflow for improved segmentation of myelin and axons in high-resolution images of human cerebral white matter.

Journal of neuroscience methods
BACKGROUND: Standard segmentation of high-contrast electron micrographs (EM) identifies myelin accurately but does not translate easily into measurements of individual axons and their myelin, even in cross-sections of parallel fibers. We describe aut...

Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning.

Genes
Alternative splicing (AS) is the process of combining different parts of the pre-mRNA to produce diverse transcripts and eventually different protein products from a single gene. In computational biology field, researchers try to understand AS behavi...

Leveraging Contextual Sentences for Text Classification by Using a Neural Attention Model.

Computational intelligence and neuroscience
We explored several approaches to incorporate context information in the deep learning framework for text classification, including designing different attention mechanisms based on different neural network and extracting some additional features fro...

3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In the paper, we propose a new deep learning-based method for segmenting nasopharyngeal carcinoma (NPC) in the nasopharynx from three orthogonal CT images. The proposed method introduces a cascade strategy composed of two-phase manners. In CT images,...

Deep learning-based digital subtraction angiography image generation.

International journal of computer assisted radiology and surgery
PURPOSE: Digital subtraction angiography (DSA) is a powerful technique for diagnosing cardiovascular disease. In order to avoid image artifacts caused by patient movement during imaging, we take deep learning-based methods to generate DSA image from ...

Combining patient visual timelines with deep learning to predict mortality.

PloS one
BACKGROUND: Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representati...

Performance of deep learning for differentiating pancreatic diseases on contrast-enhanced magnetic resonance imaging: A preliminary study.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to evaluate the ability of deep learning to differentiate pancreatic diseases on contrast-enhanced magnetic resonance (MR) images with the aid of generative adversarial network (GAN).

Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: This study demonstrates deep learning approaches with an aim to find the optimal method to automatically detect sleep apnea (SA) events from an electrocardiogram (ECG) signal.

Retinal image assessment using bi-level adaptive morphological component analysis.

Artificial intelligence in medicine
The automated analysis of retinal images is a widely researched area which can help to diagnose several diseases like diabetic retinopathy in early stages of the disease. More specifically, separation of vessels and lesions is very critical as featur...

Using distant supervision to augment manually annotated data for relation extraction.

PloS one
Significant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are availabl...