AIMC Topic: Neural Networks, Computer

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Segmentation of liver CT images based on weighted medical transformer model.

Scientific reports
Deep convolutional neural networks have made significant strides in the field of medical image segmentation. Although existing convolutional structures enhance performance by leveraging local image information, they often lose the interdependence inf...

Emotion recognition with reduced channels using CWT based EEG feature representation and a CNN classifier.

Biomedical physics & engineering express
Although emotion recognition has been studied for decades, a more accurate classification method that requires less computing is still needed. At present, in many studies, EEG features are extracted from all channels to recognize emotional states, ho...

Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network.

Neural plasticity
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms underlying ADHD remain inadequately understood, and current approaches...

Analysis of Bladder Cancer Staging Prediction Using Deep Residual Neural Network, Radiomics, and RNA-Seq from High-Definition CT Images.

Genetics research
Bladder cancer has recently seen an alarming increase in global diagnoses, ascending as a predominant cause of cancer-related mortalities. Given this pressing scenario, there is a burgeoning need to identify effective biomarkers for both the diagnosi...

A simple and effective convolutional operator for node classification without features by graph convolutional networks.

PloS one
Graph neural networks (GNNs), with their ability to incorporate node features into graph learning, have achieved impressive performance in many graph analysis tasks. However, current GNNs including the popular graph convolutional network (GCN) cannot...

Pollen identification through convolutional neural networks: First application on a full fossil pollen sequence.

PloS one
The automation of pollen identification has seen vast improvements in the past years, with Convolutional Neural Networks coming out as the preferred tool to train models. Still, only a small portion of works published on the matter address the identi...

Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection.

PloS one
The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sampl...

Radiation dose estimation with multiple artificial neural networks in dicentric chromosome assay.

International journal of radiation biology
PURPOSE: The dicentric chromosome assay (DCA), often referred to as the 'gold standard' in radiation dose estimation, exhibits significant challenges as a consequence of its labor-intensive nature and dependency on expert knowledge. Existing automate...

Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction.

Interdisciplinary sciences, computational life sciences
Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting th...

Learning active subspaces and discovering important features with Gaussian radial basis functions neural networks.

Neural networks : the official journal of the International Neural Network Society
Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives. To address this ...