AIMC Topic: Deep Learning

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On-chip photonic diffractive optical neural network based on a spatial domain electromagnetic propagation model.

Optics express
An integrated physical diffractive optical neural network (DONN) is proposed based on a standard silicon-on-insulator (SOI) substrate. This DONN has compact structure and can realize the function of machine learning with whole-passive fully-optical m...

[Clinical significance of the deep learning algorithm based on contrast-enhanced CT in the differential diagnosis of gastric gastrointestinal stromal tumors with a diameter ≤ 5 cm].

Zhonghua wei chang wai ke za zhi = Chinese journal of gastrointestinal surgery
Contrast-enhanced CT is an important method of preoperative diagnosis and evaluation for the malignant potential of gastric submucosal tumor (SMT). It has a high diagnostic accuracy rate in differentiating gastric gastrointestinal stromal tumor (GIS...

Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging.

World journal of gastroenterology
BACKGROUND: The nature of input data is an essential factor when training neural networks. Research concerning magnetic resonance imaging (MRI)-based diagnosis of liver tumors using deep learning has been rapidly advancing. Still, evidence to support...

Application of Pre-Trained Deep Learning Models for Clinical ECGs.

Studies in health technology and informatics
Automatic electrocardiogram (ECG) analysis has been one of the very early use cases for computer assisted diagnosis (CAD). Most ECG devices provide some level of automatic ECG analysis. In the recent years, Deep Learning (DL) is increasingly used for...

Multi-Disease Detection in Retinal Imaging Based on Ensembling Heterogeneous Deep Learning Models.

Studies in health technology and informatics
Preventable or undiagnosed visual impairment and blindness affect billion of people worldwide. Automated multi-disease detection models offer great potential to address this problem via clinical decision support in diagnosis. In this work, we propose...

DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Research on pharmacovigilance from social media data has focused on mining adverse drug events (ADEs) using annotated datasets, with publications generally focusing on 1 of 3 tasks: ADE classification, named entity recognition for identify...

Spatio-temporal continuous gesture recognition under degraded environments: performance comparison between 3D integral imaging (InIm) and RGB-D sensors.

Optics express
In this paper, we introduce a deep learning-based spatio-temporal continuous human gesture recognition algorithm under degraded conditions using three-dimensional (3D) integral imaging. The proposed system is shown as an efficient continuous human ge...

DeePhage: distinguishing virulent and temperate phage-derived sequences in metavirome data with a deep learning approach.

GigaScience
BACKGROUND: Prokaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage-derived sequences in metavirome data is important for elucidating their different roles in interactio...

DeepLINK: Deep learning inference using knockoffs with applications to genomics.

Proceedings of the National Academy of Sciences of the United States of America
We propose a deep learning-based knockoffs inference framework, DeepLINK, that guarantees the false discovery rate (FDR) control in high-dimensional settings. DeepLINK is applicable to a broad class of covariate distributions described by the possibl...

Exploration of machine algorithms based on deep learning model and feature extraction.

Mathematical biosciences and engineering : MBE
The study expects to solve the problems of insufficient labeling, high input dimension, and inconsistent task input distribution in traditional lifelong machine learning. A new deep learning model is proposed by combining feature representation with ...