AIMC Topic: Deep Learning

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Dynamic Hierarchical Convolutional Attention Network for Recognizing Motor Imagery Intention.

IEEE transactions on cybernetics
The neural activity patterns of localized brain regions are crucial for recognizing brain intentions. However, existing electroencephalogram (EEG) decoding models, especially those based on deep learning, predominantly focus on global spatial feature...

Torg-Pavlov ratio qualification to diagnose developmental cervical spinal stenosis based on HRViT neural network.

BMC musculoskeletal disorders
BACKGROUND: Developing computer-assisted methods to measure the Torg-Pavlov ratio (TPR), defined as the ratio of the sagittal diameter of the cervical spinal canal to the sagittal diameter of the corresponding vertebral body on lateral radiographs, c...

Improved food image recognition by leveraging deep learning and data-driven methods with an application to Central Asian Food Scene.

Scientific reports
The burden of diet-related diseases is high in Central Asia. In recent years, the field of food computing has gained prominence due to advancements in computer vision (CV) and the increasing use of smartphones and social media. These technologies pro...

Ambiguity-aware semi-supervised learning for leaf disease classification.

Scientific reports
In deep learning, Semi-Supervised Learning is a highly effective technique to enhances neural network training by leveraging both labeled and unlabeled data. This process involves using a trained model to generate pseudo labels to the unlabeled sampl...

Using deep learning models to decode emotional states in horses.

Scientific reports
In this study, we explore machine learning models for predicting emotional states in ridden horses. We manually label the images to train the models in a supervised manner. We perform data exploration and use different cropping methods, mainly based ...

A hybrid variational autoencoder and WGAN with gradient penalty for tertiary protein structure generation.

Scientific reports
Elucidating the tertiary structure of proteins is important for understanding their functions and interactions. While deep neural networks have advanced the prediction of a protein's native structure from its amino acid sequence, the focus on a singl...

Single Molecule Localization Super-resolution Dataset for Deep Learning with Paired Low-resolution Images.

Scientific data
Deep learning super-resolution microscopy has advanced rapidly in recent years. Super-resolution images acquired by single molecule localization microscopy (SMLM) are ideal sources for high-quality datasets. However, the scarcity of public datasets l...

DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions.

BMC biology
BACKGROUND: Numerous studies have shown that circRNA can act as a miRNA sponge, competitively binding to miRNAs, thereby regulating gene expression and disease progression. Due to the high cost and time-consuming nature of traditional wet lab experim...

Improved Pine Wood Nematode Disease Diagnosis System Based on Deep Learning.

Plant disease
Pine wilt disease caused by the pine wood nematode, , has profound implications for global forestry ecology. Conventional PCR methods need long operating time and are complicated to perform. The need for rapid and effective detection methodologies to...

Automatic determination of the resection plane for shoulder arthroplasty in arthritic humeri: a deep learning model.

Journal of shoulder and elbow surgery
BACKGROUND: The humeral head resection plane relies on the anatomic neck as a reference for shoulder arthroplasty planning and accurate intraoperative placement of the humeral component. However, osteophytes and deformity associated with osteoarthrit...