AIMC Topic: Deep Learning

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Automatic semantic segmentation of EHG recordings by deep learning: An approach to a screening tool for use in clinical practice.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Preterm delivery is an important factor in the disease burden of the newborn and infants worldwide. Electrohysterography (EHG) has become a promising technique for predicting this condition, thanks to its high degree of sens...

Deep learning prediction of survival in patients with heart failure using chest radiographs.

The international journal of cardiovascular imaging
Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs in patients with heart failure is currently unclear. The aim of our study is to develop and val...

Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.

Cell research
Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we ...

Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches.

Medical engineering & physics
Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal...

A mathematical theory of relational generalization in transitive inference.

Proceedings of the National Academy of Sciences of the United States of America
Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced co...

Glove-Net: Enhancing Grasp Classification with Multisensory Data and Deep Learning Approach.

Sensors (Basel, Switzerland)
Grasp classification is pivotal for understanding human interactions with objects, with wide-ranging applications in robotics, prosthetics, and rehabilitation. This study introduces a novel methodology utilizing a multisensory data glove to capture i...

Deepvirusclassifier: a deep learning tool for classifying SARS-CoV-2 based on viral subtypes within the coronaviridae family.

BMC bioinformatics
PURPOSE: In this study, we present DeepVirusClassifier, a tool capable of accurately classifying Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral sequences among other subtypes of the coronaviridae family. This classification is ach...

PND-Net: plant nutrition deficiency and disease classification using graph convolutional network.

Scientific reports
Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress....

An appearance quality classification method for Auricularia auricula based on deep learning.

Scientific reports
The intelligent appearance quality classification method for Auricularia auricula is of great significance to promote this industry. This paper proposes an appearance quality classification method for Auricularia auricula based on the improved Faster...

Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images.

Frontiers in immunology
BACKGROUND: Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning sys...