AIMC Topic: Deep Learning

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Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks.

Scientific reports
The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive d...

Examining the Use of an Artificial Intelligence Model to Diagnose Influenza: Development and Validation Study.

Journal of medical Internet research
BACKGROUND: The global burden of influenza is substantial. It is a major disease that causes annual epidemics and occasionally, pandemics. Given that influenza primarily infects the upper respiratory system, it may be possible to diagnose influenza i...

A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded.

Nature biomedical engineering
Histological artefacts in cryosectioned tissue can hinder rapid diagnostic assessments during surgery. Formalin-fixed and paraffin-embedded (FFPE) tissue provides higher quality slides, but the process for obtaining them is laborious (typically lasti...

A deep learning platform to assess drug proarrhythmia risk.

Cell stem cell
Drug safety initiatives have endorsed human iPSC-derived cardiomyocytes (hiPSC-CMs) as an in vitro model for predicting drug-induced cardiac arrhythmia. However, the extent to which human-defined features of in vitro arrhythmia predict actual clinica...

Behaviour recommendations with a deep learning model and genetic algorithm for health debt characterisation.

Journal of biomedical informatics
Human behaviour is a dense longitudinal multi-featured measure that directly impacts the health of individuals in the short and long terms. Therefore, issues usually emerge from the insistence on performing risky behaviours, such as smoking or eating...

Self-mentoring: A new deep learning pipeline to train a self-supervised U-net for few-shot learning of bio-artificial capsule segmentation.

Computers in biology and medicine
BACKGROUND: Accurate segmentation of microscopic structures such as bio-artificial capsules in microscopy imaging is a prerequisite to the computer-aided understanding of important biomechanical phenomenons. State-of-the-art segmentation performances...

Development of Gestational Age-Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Fetal brain MR imaging interpretations are subjective and require subspecialty expertise. We aimed to develop a deep learning algorithm for automatically measuring intracranial and brain volumes of fetal brain MRIs across gest...

Conceptual Structure and Current Trends in Artificial Intelligence, Machine Learning, and Deep Learning Research in Sports: A Bibliometric Review.

International journal of environmental research and public health
Artificial intelligence and its subcategories of machine learning and deep learning are gaining increasing importance and attention in the context of sports research. This has also meant that the number of corresponding publications has become comple...

LiSA: an assisted literature search pipeline for detecting serious adverse drug events with deep learning.

BMC medical informatics and decision making
INTRODUCTION: Detecting safety signals attributed to a drug in scientific literature is a fundamental issue in pharmacovigilance. The constant increase in the volume of publications requires the automation of this tedious task, in order to find and e...

Symptoms are known by their companies: towards association guided disease diagnosis assistant.

BMC bioinformatics
Over the last few years, dozens of healthcare surveys have shown a shortage of doctors and an alarming doctor-population ratio. With the motivation of assisting doctors and utilizing their time efficiently, automatic disease diagnosis using artificia...