AIMC Topic: Humans

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Uncertainty-Aware Knowledge Distillation for Collision Identification of Collaborative Robots.

Sensors (Basel, Switzerland)
Human-robot interaction has received a lot of attention as collaborative robots became widely utilized in many industrial fields. Among techniques for human-robot interaction, collision identification is an indispensable element in collaborative robo...

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases.

International journal of molecular sciences
Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficien...

Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network.

Scientific reports
To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined s...

Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques.

Scientific reports
Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to efficiently predict outcomes of both the Mini-Mental State Examination (MMSE) and Montreal Cognitive A...

Annotation-efficient deep learning for automatic medical image segmentation.

Nature communications
Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to...

Protein-protein interaction prediction based on ordinal regression and recurrent convolutional neural networks.

BMC bioinformatics
BACKGROUND: Protein protein interactions (PPIs) are essential to most of the biological processes. The prediction of PPIs is beneficial to the understanding of protein functions and thus is helpful to pathological analysis, disease diagnosis and drug...

StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors.

Journal of computer-aided molecular design
Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often...

A Liver Segmentation Method Based on the Fusion of VNet and WGAN.

Computational and mathematical methods in medicine
Accurate segmentation of liver images is an essential step in liver disease diagnosis, treatment planning, and prognosis. In recent years, although liver segmentation methods based on 2D convolutional neural networks have achieved good results, there...

SMBFT: A Modified Fuzzy -Means Algorithm for Superpixel Generation.

Computational and mathematical methods in medicine
Most traditional superpixel segmentation methods used binary logic to generate superpixels for natural images. When these methods are used for images with significantly fuzzy characteristics, the boundary pixels sometimes cannot be correctly classifi...

Protein Family Classification from Scratch: A CNN Based Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics
Next-generation sequencing techniques provide us with an opportunity for generating sequenced proteins and identifying the biological families and functions of these proteins. However, compared with identified proteins, uncharacterized proteins consi...