AIMC Topic: Neural Networks, Computer

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Surrogate modeling of electrospun PVA/PLA nanofibers using artificial neural network for biomedical applications.

Scientific reports
Blending poly (lactic acid) (PLA) with poly (vinyl alcohol) (PVA) improves the strength and hydrophilicity of nanofibers, making them suitable for biomedical applications like wound dressings. This study explores how electrospinning parameters-applie...

Multi-scale prototype convolutional network for few-shot semantic segmentation.

PloS one
Few-shot semantic segmentation aims to accurately segment objects from a limited amount of annotated data, a task complicated by intra-class variations and prototype representation challenges. To address these issues, we propose the Multi-Scale Proto...

Prediction of newly synthesized heparin mimic's effects as heparanase inhibitor in cancer treatments via variational quantum neural networks.

Computational biology and chemistry
Cancer remains a leading global cause of death, primarily driven by the uncontrolled proliferation of abnormal cells. Malignant tumors, such as carcinomas, originate from unchecked epithelial cell growth and produce growth factors like FGF and VEGF, ...

Unsupervised alignment in neuroscience: Introducing a toolbox for Gromov-Wasserstein optimal transport.

Journal of neuroscience methods
BACKGROUND: Understanding how sensory stimuli are represented across different brains, species, and artificial neural networks is a critical topic in neuroscience. Traditional methods for comparing these representations typically rely on supervised a...

Utilizing Pix2Pix conditional generative adversarial networks to recover missing data in preclinical PET scanner sinogram gaps.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
BACKGROUND: The presence of a gap between adjacent detector blocks in Positron Emission Tomography (PET) scanners introduces a partial loss of projection data, which can degrade the image quality and quantitative accuracy of reconstructed PET images....

Crystal Structure Prediction Using a Self-Attention Neural Network and Semantic Segmentation.

Journal of chemical information and modeling
The development of new materials is a time-consuming and resource-intensive process. Deep learning has emerged as a promising approach to accelerate this process. However, accurately predicting crystal structures using deep learning remains a signifi...

Towards interpretable sleep stage classification with a multi-stream fusion network.

BMC medical informatics and decision making
Sleep stage classification is a significant measure in assessing sleep quality and diagnosing sleep disorders. Many researchers have investigated automatic sleep stage classification methods and achieved promising results. However, these methods igno...

Exploring the potential of cell-free RNA and Pyramid Scene Parsing Network for early preeclampsia screening.

BMC pregnancy and childbirth
BACKGROUND: Circulating cell-free RNA (cfRNA) is gaining recognition as an effective biomarker for the early detection of preeclampsia (PE). However, the current methods for selecting disease-specific biomarkers are often inefficient and typically on...

Transformer-based deep learning for accurate detection of multiple base modifications using single molecule real-time sequencing.

Communications biology
We had previously reported a convolutional neural network (CNN) based approach, called the holistic kinetic model (HK model 1), for detecting 5-methylcytosine (5mC) by single molecule real-time sequencing (Pacific Biosciences). In this study, we cons...

A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides.

Scientific reports
Current artificial intelligence (AI) trends are revolutionizing medical image processing, greatly improving cervical cancer diagnosis. Machine learning (ML) algorithms can discover patterns and anomalies in medical images, whereas deep learning (DL) ...