AIMC Topic: Deep Learning

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Deep Learning-Based Brain Hemorrhage Detection in CT Reports.

Studies in health technology and informatics
Radiology reports can potentially be used to detect critical cases that need immediate attention from physicians. We focus on detecting Brain Hemorrhage from Computed Tomography (CT) reports. We train a deep learning classifier and observe the effect...

Enriching UMLS-Based Phenotyping of Rare Diseases Using Deep-Learning: Evaluation on Jeune Syndrome.

Studies in health technology and informatics
The wide adoption of Electronic Health Records (EHR) in hospitals provides unique opportunities for high throughput phenotyping of patients. The phenotype extraction from narrative reports can be performed by using either dictionary-based or data-dri...

A Deep Learning Method for Automatic Identification of Drusen and Macular Hole from Optical Coherence Tomography.

Studies in health technology and informatics
Deep Learning methods have become dominant in various fields of medical imaging, including ophthalmology. In this preliminary study, we investigated a method based on Convolutional Neural Network for the identification of drusen and macular hole from...

Using Machine Learning and Deep Learning Methods to Predict the Complexity of Breast Cancer Cases.

Studies in health technology and informatics
In many countries, the management of cancer patients must be discussed in multidisciplinary tumor boards (MTBs). These meetings have been introduced to provide a collaborative and multidisciplinary approach to cancer care. However, the benefits of MT...

Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning.

The Plant cell
In the evolutionary history of plants, variation in cis-regulatory elements (CREs) resulting in diversification of gene expression has played a central role in driving the evolution of lineage-specific traits. However, it is difficult to predict expr...

Hybrid-structure network and network comparative study for deep-learning-based speckle-modulating optical coherence tomography.

Optics express
Optical coherence tomography (OCT), a promising noninvasive bioimaging technique, can resolve sample three-dimensional microstructures. However, speckle noise imposes obvious limitations on OCT resolving capabilities. Here we proposed a deep-learning...

Deep learning tools are top performers in long non-coding RNA prediction.

Briefings in functional genomics
The increasing amount of transcriptomic data has brought to light vast numbers of potential novel RNA transcripts. Accurately distinguishing novel long non-coding RNAs (lncRNAs) from protein-coding messenger RNAs (mRNAs) has challenged bioinformatic ...

[Study on the accuracy of automatic segmentation of knee CT images based on deep learning].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery
OBJECTIVE: To develop a neural network architecture based on deep learning to assist knee CT images automatic segmentation, and validate its accuracy.