AIMC Topic: Algorithms

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Neural models for detection and classification of brain states and transitions.

Communications biology
Exploring natural or pharmacologically induced brain dynamics, such as sleep, wakefulness, or anesthesia, provides rich functional models for studying brain states. These models allow detailed examination of unique spatiotemporal neural activity patt...

Evaluating the dosimetric and positioning accuracy of a deep learning based synthetic-CT model for liver radiotherapy treatment planning.

Biomedical physics & engineering express
An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient positioning accuracy of deep learning-generated sCT for liver radiotherapy.sCT images were generated ...

Development of a Machine Learning Algorithm to Predict Abnormalities in Serum Phosphate in a Large Oncology Cohort.

JCO clinical cancer informatics
PURPOSE: Serum phosphate is commonly measured in oncology patients because of the relationship between oncologic conditions and treatments with abnormal phosphate. All patients attending our institution, a large specialist oncology center, have a sta...

CWMS-GAN: A small-sample bearing fault diagnosis method based on continuous wavelet transform and multi-size kernel attention mechanism.

PloS one
In industrial production, obtaining sufficient bearing fault signals is often extremely difficult, leading to a significant degradation in the performance of traditional deep learning-based fault diagnosis models. Many recent studies have shown that ...

SSA-classifier based screening study for Alzheimer's disease.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Alzheimer's is a disease (AD) that affects 10 % of individuals aged ≥ 65, is the most prevalent neurodegenerative disorder. We propose a diagnostic framework integrating plasma attenuated total reflection Fourier transform infrared (ATR-FTIR) spectro...

Solving two-stage stochastic integer programs via representation learning.

Neural networks : the official journal of the International Neural Network Society
Solving stochastic integer programs (SIPs) is extremely intractable due to the high computational complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) for scenario representation learning. A graph c...

BC-PMJRS: A Brain Computing-inspired Predefined Multimodal Joint Representation Spaces for enhanced cross-modal learning.

Neural networks : the official journal of the International Neural Network Society
Multimodal learning faces two key challenges: effectively fusing complex information from different modalities, and designing efficient mechanisms for cross-modal interactions. Inspired by neural plasticity and information processing principles in th...

Generating realistic single-cell images from CellProfiler representations.

Medical image analysis
High-throughput imaging techniques acquire large amounts of images efficiently. These images contain rich biological information including cellular processes. A common method to analyse them is to encode them into quantitative representation vectors....

Infrared thermography of beef carcasses and random forest algorithm to predict temperature and pH of Longissimus thoracis on carcasses.

Meat science
This study aimed to evaluate the use of infrared thermography (IRT) as a method for predicting the initial and ultimate temperature, as well as the pH, of the Longissimus thoracis in beef carcasses (LTBC). A total of 102 beef carcasses, consisting of...

μGlia-Flow, an automatic workflow for microglia segmentation and classification.

Journal of neuroscience methods
BACKGROUND: Microglia are important immune cells in the central nervous system, playing a key role in various pathological processes. The morphological diversity of microglia is closely linked to the development of brain diseases, yet accurate segmen...