AIMC Topic: Neural Networks, Computer

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Crash injury severity prediction considering data imbalance: A Wasserstein generative adversarial network with gradient penalty approach.

Accident; analysis and prevention
For each road crash event, it is necessary to predict its injury severity. However, predicting crash injury severity with the imbalanced data frequently results in ineffective classifier. Due to the rarity of severe injuries in road traffic crashes, ...

Weakly Supervised Temporal Convolutional Networks for Fine-Grained Surgical Activity Recognition.

IEEE transactions on medical imaging
Automatic recognition of fine-grained surgical activities, called steps, is a challenging but crucial task for intelligent intra-operative computer assistance. The development of current vision-based activity recognition methods relies heavily on a h...

Robust Prototypical Few-Shot Organ Segmentation With Regularized Neural-ODEs.

IEEE transactions on medical imaging
Despite the tremendous progress made by deep learning models in image semantic segmentation, they typically require large annotated examples, and increasing attention is being diverted to problem settings like Few-Shot Learning (FSL) where only a sma...

H2Former: An Efficient Hierarchical Hybrid Transformer for Medical Image Segmentation.

IEEE transactions on medical imaging
Accurate medical image segmentation is of great significance for computer aided diagnosis. Although methods based on convolutional neural networks (CNNs) have achieved good results, it is weak to model the long-range dependencies, which is very impor...

SCANet: A Unified Semi-Supervised Learning Framework for Vessel Segmentation.

IEEE transactions on medical imaging
Automatic subcutaneous vessel imaging with near-infrared (NIR) optical apparatus can promote the accuracy of locating blood vessels, thus significantly contributing to clinical venipuncture research. Though deep learning models have achieved remarkab...

Application of artificial neural network in daily prediction of bleeding in ICU patients treated with anti-thrombotic therapy.

BMC medical informatics and decision making
OBJECTIVES: Anti-thrombotic therapy is the basis of thrombosis prevention and treatment. Bleeding is the main adverse event of anti-thrombosis. Existing laboratory indicators cannot accurately reflect the real-time coagulation function. It is necessa...

E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction.

PLoS computational biology
Artificial intelligence-powered protein structure prediction methods have led to a paradigm-shift in computational structural biology, yet contemporary approaches for predicting the interfacial residues (i.e., sites) of protein-protein interaction (P...

Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity.

PloS one
Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing ta...

Value of Artificial Intelligence in Improving the Accuracy of Diagnosing TI-RADS Category 4 Nodules.

Ultrasound in medicine & biology
OBJECTIVE: Considerable heterogeneity is observed in the malignancy rates of thyroid nodules classified as category 4 according to the Thyroid Imaging Reporting and Data System (TI-RADS). This study was aimed at comparing the diagnostic performance o...

Anthropogenic fingerprints in daily precipitation revealed by deep learning.

Nature
According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe. However, verifying this prediction using observations has remained a substantial challenge owing to lar...