AIMC Topic: Deep Learning

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Parkinson's Disease Tremor Detection in the Wild Using Wearable Accelerometers.

Sensors (Basel, Switzerland)
Continuous in-home monitoring of Parkinson's Disease (PD) symptoms might allow improvements in assessment of disease progression and treatment effects. As a first step towards this goal, we evaluate the feasibility of a wrist-worn wearable accelerome...

How much off-the-shelf knowledge is transferable from natural images to pathology images?

PloS one
Deep learning has achieved a great success in natural image classification. To overcome data-scarcity in computational pathology, recent studies exploit transfer learning to reuse knowledge gained from natural images in pathology image analysis, aimi...

Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss.

Sensors (Basel, Switzerland)
Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discrim...

Retinal Boundary Segmentation in Stargardt Disease Optical Coherence Tomography Images Using Automated Deep Learning.

Translational vision science & technology
PURPOSE: To use a deep learning model to develop a fully automated method (fully semantic network and graph search [FS-GS]) of retinal segmentation for optical coherence tomography (OCT) images from patients with Stargardt disease.

Automated detection of cerebral microbleeds in MR images: A two-stage deep learning approach.

NeuroImage. Clinical
Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. However, automated detectio...

A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre.

Nature biomedical engineering
Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using divers...

Deep learning in next-generation sequencing.

Drug discovery today
Next-generation sequencing (NGS) methods lie at the heart of large parts of biological and medical research. Their fundamental importance has created a continuously increasing demand for processing and analysis methods of the data sets produced, addr...

Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats.

Environmental monitoring and assessment
Environmental monitoring guides conservation and is particularly important for aquatic habitats which are heavily impacted by human activities. Underwater cameras and uncrewed devices monitor aquatic wildlife, but manual processing of footage is a si...

Activity prediction of aminoquinoline drugs based on deep learning.

Biotechnology and applied biochemistry
The results of the traditional prediction method for the activity of aminoquinoline drugs are inaccurate, so the prediction method for the activity of aminoquinoline drugs based on the deep learning is designed. The molecular holographic distance vec...