AIMC Topic: Neural Networks, Computer

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Artificial intelligence-based diagnosis of standard endoscopic ultrasonography scanning sites in the biliopancreatic system: a multicenter retrospective study.

International journal of surgery (London, England)
BACKGROUND: There are challenges for beginners to identify standard biliopancreatic system anatomical sites on endoscopic ultrasonography (EUS) images. Therefore, the authors aimed to develop a convolutional neural network (CNN)-based model to identi...

Identification and segregation of genes with improved recurrent neural network trained with optimal gene level and mutation level features.

Computer methods in biomechanics and biomedical engineering
Even though many different approaches have been employed to address the complex mutational heterogeneity of cancer, finding driver genes is still problematic since other genomic factors cannot be fully integrated for combined analyses. This research ...

Advances in AI and machine learning for predictive medicine.

Journal of human genetics
The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data ana...

A Hybrid Framework of Dual-Domain Signal Restoration and Multi-depth Feature Reinforcement for Low-Dose Lung CT Denoising.

Journal of imaging informatics in medicine
Low-dose computer tomography (LDCT) has been widely used in medical diagnosis. Various denoising methods have been presented to remove noise in LDCT scans. However, existing methods cannot achieve satisfactory results due to the difficulties in (1) d...

Segmentation, feature extraction and classification of leukocytes leveraging neural networks, a comparative study.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The gold standard of leukocyte differentiation is a manual examination of blood smears, which is not only time and labor intensive but also susceptible to human error. As to automatic classification, there is still no comparative study of cell segmen...

Enhancing neural encoding models for naturalistic perception with a multi-level integration of deep neural networks and cortical networks.

Science bulletin
Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks (DNNs) to predic...

Miffi: Improving the accuracy of CNN-based cryo-EM micrograph filtering with fine-tuning and Fourier space information.

Journal of structural biology
Efficient and high-accuracy filtering of cryo-electron microscopy (cryo-EM) micrographs is an emerging challenge with the growing speed of data collection and sizes of datasets. Convolutional neural networks (CNNs) are machine learning models that ha...

12-Lead ECG Reconstruction Based on Data From the First Limb Lead.

Cardiovascular engineering and technology
PURPOSE: Electrocardiogram (ECG) data obtained from 12 leads are the most common and informative source for analyzing the cardiovascular system's (CVS) condition in medical practice. However, the large number of electrodes, specific placements on the...

Convolutions are competitive with transformers for protein sequence pretraining.

Cell systems
Pretrained protein sequence language models have been shown to improve the performance of many prediction tasks and are now routinely integrated into bioinformatics tools. However, these models largely rely on the transformer architecture, which scal...

Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks.

Journal of chemical information and modeling
Molecular design depends heavily on optical properties for applications such as solar cells and polymer-based batteries. Accurate prediction of these properties is essential, and multiple predictive methods exist, from to data-driven techniques. Alt...