AIMC Topic: Neural Networks, Computer

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CapNet: An Automatic Attention-Based with Mixer Model for Cardiovascular Magnetic Resonance Image Segmentation.

Journal of imaging informatics in medicine
Deep neural networks have shown excellent performance in medical image segmentation, especially for cardiac images. Transformer-based models, though having advantages over convolutional neural networks due to the ability of long-range dependence lear...

AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
PURPOSE: Early and accurate assessment of distal radius fractures (DRFs) is crucial for optimal prognosis. Identifying fractures likely to lose threshold alignment (instability) in a cast is vital for treatment decisions, yet prediction tools' accura...

Structural prior-driven feature extraction with gradient-momentum combined optimization for convolutional neural network image classification.

Neural networks : the official journal of the International Neural Network Society
Recent image classification efforts have achieved certain success by incorporating prior information such as labels and logical rules to learn discriminative features. However, these methods overlook the variability of features, resulting in feature ...

Probability graph complementation contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Network (GNN) has achieved remarkable progress in the field of graph representation learning. The most prominent characteristic, propagating features along the edges, degrades its performance in most heterophilic graphs. Certain research...

A weighted prior tensor train decomposition method for community detection in multi-layer networks.

Neural networks : the official journal of the International Neural Network Society
Community detection in multi-layer networks stands as a prominent subject within network analysis research. However, the majority of existing techniques for identifying communities encounter two primary constraints: they lack suitability for high-dim...

T-distributed Stochastic Neighbor Network for unsupervised representation learning.

Neural networks : the official journal of the International Neural Network Society
Unsupervised representation learning (URL) is still lack of a reasonable operator (e.g. convolution kernel) for exploring meaningful structural information from generic data including vector, image and tabular data. In this paper, we propose a simple...

Multi-task neural networks by learned contextual inputs.

Neural networks : the official journal of the International Neural Network Society
This paper explores learned-context neural networks. It is a multi-task learning architecture based on a fully shared neural network and an augmented input vector containing trainable task parameters. The architecture is interesting due to its powerf...

Mining core information by evaluating semantic importance for unpaired image captioning.

Neural networks : the official journal of the International Neural Network Society
Recently, exciting progress has been made in the research of supervised image captioning. However, manually annotated image-annotation pair data is difficult and expensive to obtain. Therefore, unpaired image captioning becomes an emerging challenge....

Development and evaluation of a deep neural network model for orthokeratology lens fitting.

Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)
PURPOSE: To optimise the precision and efficacy of orthokeratology, this investigation evaluated a deep neural network (DNN) model for lens fitting. The objective was to refine the standardisation of fitting procedures and curtail subjective evaluati...

Prediction of retention data of phenolic compounds by quantitative structure retention relationship models under reverse-phase liquid chromatography.

Journal of chromatography. A
Quantitative Structure-Retention Relationship models were developed to identify phenolic compounds using a typical LC- system, with both UV and MS detection. A new chromatographic method was developed for the separation of fifty-two standard phenolic...