AIMC Topic: Neural Networks, Computer

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Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Knee osteoarthritis (OA) is one major cause of activity limitation and physical disability in older adults. Early detection and intervention can help slow down the OA degeneration. Physicians' grading based on visual inspection is subjective, varied ...

Deep Learning in Chemistry.

Journal of chemical information and modeling
Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns represented in t...

Predicting instances of pathway ontology classes for pathway integration.

Journal of biomedical semantics
BACKGROUND: To improve the outcomes of biological pathway analysis, a better way of integrating pathway data is needed. Ontologies can be used to organize data from disparate sources, and we leverage the Pathway Ontology as a unifying ontology for or...

Prediction of activity and specificity of CRISPR-Cpf1 using convolutional deep learning neural networks.

BMC bioinformatics
BACKGROUND: CRISPR-Cpf1 has recently been reported as another RNA-guided endonuclease of class 2 CRISPR-Cas system, which expands the molecular biology toolkit for genome editing. However, most of the online tools and applications to date have been d...

A Deep Convolutional Neural Network Approach to Classify Normal and Abnormal Gastric Slow Wave Initiation From the High Resolution Electrogastrogram.

IEEE transactions on bio-medical engineering
OBJECTIVE: Gastric slow wave abnormalities have been associated with gastric motility disorders. Invasive studies in humans have described normal and abnormal propagation of the slow wave. This study aims to disambiguate the abnormally functioning wa...

Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications.

Clinical radiology
Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main ...

A cascaded dual-pathway residual network for lung nodule segmentation in CT images.

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)
It is difficult to obtain an accurate segmentation due to the variety of lung nodules in computed tomography (CT) images. In this study, we propose a data-driven model, called the Cascaded Dual-Pathway Residual Network (CDP-ResNet) to improve the seg...

Learning image-based spatial transformations via convolutional neural networks: A review.

Magnetic resonance imaging
Recent methodological innovations in deep learning and associated advancements in computational hardware have significantly impacted the various core subfields of quantitative medical image analysis. The generalizability, computational efficiency and...

Acuity VEP: improved with machine learning.

Documenta ophthalmologica. Advances in ophthalmology
PURPOSE: Acuity-VEP approaches basically all use the information obtained across a number of check sizes (or spatial frequencies) to derive a measure of acuity. Amplitude is always used, sometimes combined with phase or a noise measure. In our approa...