AIMC Topic: Deep Learning

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MFP-Unet: A novel deep learning based approach for left ventricle segmentation in echocardiography.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Segmentation of the Left ventricle (LV) is a crucial step for quantitative measurements such as area, volume, and ejection fraction. However, the automatic LV segmentation in 2D echocardiographic images is a challenging task due to ill-defined border...

A deep learning framework for neuroscience.

Nature neuroscience
Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In ...

Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.

Nature biomedical engineering
Tomographic imaging using penetrating waves generates cross-sectional views of the internal anatomy of a living subject. For artefact-free volumetric imaging, projection views from a large number of angular positions are required. Here we show that a...

Heart Sound Segmentation Using Bidirectional LSTMs With Attention.

IEEE journal of biomedical and health informatics
OBJECTIVE: This paper proposes a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states, exploiting the temporal evolution of the PCG as well as considering the salient information that it provides for the detection o...

ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial.

American heart journal
BACKGROUND: A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automaticall...

CapsCarcino: A novel sparse data deep learning tool for predicting carcinogens.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
Determining chemical carcinogenicity in the early stages of drug discovery is fundamentally important to prevent the adverse effect of carcinogens on human health. There has been a recent surge of interest in developing computational approaches to pr...

Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models.

Journal of chemical information and modeling
Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem...

Deep learning enables automated volumetric assessments of cardiac function in zebrafish.

Disease models & mechanisms
Although the zebrafish embryo is a powerful animal model of human heart failure, the methods routinely employed to monitor cardiac function produce rough approximations that are susceptible to bias and inaccuracies. We developed and validated a deep ...

Development and validation of deep learning algorithms for scoliosis screening using back images.

Communications biology
Adolescent idiopathic scoliosis is the most common spinal disorder in adolescents with a prevalence of 0.5-5.2% worldwide. The traditional methods for scoliosis screening are easily accessible but require unnecessary referrals and radiography exposur...

Prediction of sgRNA on-target activity in bacteria by deep learning.

BMC bioinformatics
BACKGROUND: One of the main challenges for the CRISPR-Cas9 system is selecting optimal single-guide RNAs (sgRNAs). Recently, deep learning has enhanced sgRNA prediction in eukaryotes. However, the prokaryotic chromatin structure is different from euk...