AIMC Topic: Neural Networks, Computer

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H2MaT-Unet:Hierarchical hybrid multi-axis transformer based Unet for medical image segmentation.

Computers in biology and medicine
Accurate segmentation and lesion localization are essential for treating diseases in medical images. Despite deep learning methods enhancing segmentation, they still have limitations due to convolutional neural networks' inability to capture long-ran...

Graphical user interface-based convolutional neural network models for detecting nasopalatine duct cysts using panoramic radiography.

Scientific reports
Nasopalatine duct cysts are difficult to detect on panoramic radiographs due to obstructive shadows and are often overlooked. Therefore, sensitive detection using panoramic radiography is clinically important. This study aimed to create a trained mod...

Protein language model-embedded geometric graphs power inter-protein contact prediction.

eLife
Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions. Although significant improvement has been made in inter-protein contact prediction recently, ...

Simplified detection of genetic background admixture using artificial intelligence.

Clinical genetics
Admixture refers to the mixing of genetic ancestry from different populations. Admixture is important for genomic medicine because it can affect how an individual responds to certain medications, how they metabolize drugs, and susceptibility to certa...

Structural deep multi-view clustering with integrated abstraction and detail.

Neural networks : the official journal of the International Neural Network Society
Deep multi-view clustering, which can obtain complementary information from different views, has received considerable attention in recent years. Although some efforts have been made and achieve decent performances, most of them overlook the structur...

A novel interactive deep cascade spectral graph convolutional network with multi-relational graphs for disease prediction.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have recently grown in popularity for disease prediction. Existing GNN-based methods primarily build the graph topological structure around a single modality and combine it with other modalities to acquire feature represe...

Machine learning and statistical physics modeling of tetracycline adsorption using activated carbon derived from Cynometra ramiflora fruit biomass.

Environmental research
The current investigation reports the usage of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), the two recognized machine learning techniques in modelling tetracycline (TC) adsorption onto Cynometra ramiflora fruit ...

Multimodal semi-supervised learning for online recognition of multi-granularity surgical workflows.

International journal of computer assisted radiology and surgery
Purpose Surgical workflow recognition is a challenging task that requires understanding multiple aspects of surgery, such as gestures, phases, and steps. However, most existing methods focus on single-task or single-modal models and rely on costly an...

Using neural networks to autonomously assess adequacy in intraoperative cholangiograms.

Surgical endoscopy
BACKGROUND: Intraoperative cholangiography (IOC) is a contrast-enhanced X-ray acquired during laparoscopic cholecystectomy. IOC images the biliary tree whereby filling defects, anatomical anomalies and duct injuries can be identified. In Australia, I...

BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping.

NeuroImage
Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms...