AIMC Topic: Deep Learning

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Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection.

IEEE transactions on medical imaging
Lymph node metastasis is one of the most important indicators in breast cancer diagnosis, that is traditionally observed under the microscope by pathologists. In recent years, with the dramatic advance of high-throughput scanning and deep learning te...

BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies.

BMC bioinformatics
BACKGROUND: Recent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. I...

High-performance medicine: the convergence of human and artificial intelligence.

Nature medicine
The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to ha...

A guide to deep learning in healthcare.

Nature medicine
Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can im...

Identifying facial phenotypes of genetic disorders using deep learning.

Nature medicine
Syndromic genetic conditions, in aggregate, affect 8% of the population. Many syndromes have recognizable facial features that are highly informative to clinical geneticists. Recent studies show that facial analysis technologies measured up to the ca...

Deep learning image recognition enables efficient genome editing in zebrafish by automated injections.

PloS one
One of the most popular techniques in zebrafish research is microinjection. This is a rapid and efficient way to genetically manipulate early developing embryos, and to introduce microbes, chemical compounds, nanoparticles or tracers at larval stages...

Application of deep convolutional neural networks in classification of protein subcellular localization with microscopy images.

Genetic epidemiology
Single-cell microscopy image analysis has proved invaluable in protein subcellular localization for inferring gene/protein function. Fluorescent-tagged proteins across cellular compartments are tracked and imaged in response to genetic or environment...

DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Rodents engage in social communication through a rich repertoire of ultrasonic vocalizations (USVs). Recording and analysis of USVs has broad utility during diverse behavioral tests and can be performed noninvasively in almost any rodent behavioral m...

Fully automated detection of retinal disorders by image-based deep learning.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: With the aging population and the global diabetes epidemic, the prevalence of age-related macular degeneration (AMD) and diabetic macular edema (DME) diseases which are the leading causes of blindness is further increasing. Intravitreal inje...

Artificial intelligence for sex determination of skeletal remains: Application of a deep learning artificial neural network to human skulls.

Journal of forensic and legal medicine
A deep learning artificial neural network was adapted to the task of sex determination of skeletal remains. The neural network was trained on images of 900 skulls virtually reconstructed from hospital CT scans. When tested on previously unseen images...