AIMC Topic: Neural Networks, Computer

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Self-supervised learning-based Multi-Scale feature Fusion Network for survival analysis from whole slide images.

Computers in biology and medicine
Understanding prognosis and mortality is critical for evaluating the treatment plan of patients. Advances in digital pathology and deep learning techniques have made it practical to perform survival analysis in whole slide images (WSIs). Current meth...

Towards Single Camera Human 3D-Kinematics.

Sensors (Basel, Switzerland)
Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps t...

Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging.

Sensors (Basel, Switzerland)
We present a new deep learning framework for removing honeycomb artifacts yielded by optical path blocking of cladding layers in fiber bundle imaging. The proposed framework, HAR-CNN, provides an end-to-end mapping from a raw fiber bundle image to an...

Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals.

Sensors (Basel, Switzerland)
Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (B...

An accurate deep learning model for wheezing in children using real world data.

Scientific reports
Auscultation is an important diagnostic method for lung diseases. However, it is a subjective modality and requires a high degree of expertise. To overcome this constraint, artificial intelligence models are being developed. However, these models req...

Farmland quality assessment using deep fully convolutional neural networks.

Environmental monitoring and assessment
Farmland is the cornerstone of agriculture and is important for food security and social production. Farmland assessment is essential but traditional methods are usually expensive and slow. Deep learning methods have been developed and widely applied...

Entity relationship extraction from Chinese electronic medical records based on feature augmentation and cascade binary tagging framework.

Mathematical biosciences and engineering : MBE
Extracting entity relations from unstructured Chinese electronic medical records is an important task in medical information extraction. However, Chinese electronic medical records mostly have document-level volumes, and existing models are either un...

In-silico generation of high-dimensional immune response data in patients using a deep neural network.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches t...

Modelling and optimization study to improve the filtration performance of fibrous filter.

Chemosphere
Fibrous filter made up of non-woven material was utilized in many industrial applications for increasing the collection efficiency and the quality factor. But there exists a competing effect among the fibre diameter, filtration efficiency, pressure d...

Prediction of coronary heart disease in gout patients using machine learning models.

Mathematical biosciences and engineering : MBE
Growing evidence shows that there is an increased risk of cardiovascular diseases among gout patients, especially coronary heart disease (CHD). Screening for CHD in gout patients based on simple clinical factors is still challenging. Here we aim to b...