AIMC Topic: Deep Learning

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Detection of aging-induced vascular remodeling based on Raman imaging and deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Vascular aging-related remodeling is a common pathological basis for many chronic diseases, so early detection of physical arterial aging is important for their prevention and control. Existing staining methods can only analyze a limited number of ti...

Determination of Skeletal Age From Hand Radiographs Using Deep Learning.

The American journal of sports medicine
BACKGROUND: Surgeons treating skeletally immature patients use skeletal age to determine appropriate surgical strategies. Traditional bone age estimation methods utilizing hand radiographs are time-consuming.

Enhanced bi-branch deep learning network for in vivo hyperspectral imaging recognition of organs and tissues.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Hyperspectral imaging, as an emerging medical imaging technology, offers significant potential in biomedical research due to its ability in capturing rich spectral information. An enhanced bi-branch network integrating graph convolutional network (GC...

HySwinFormer: A hybrid deep learning architecture for fine-grained classification of marine microalgae.

The Science of the total environment
Accurate identification of microalgae is vital for marine ecological monitoring, algal bloom early warning, and environmental management. However, existing methods often struggle with misclassification due to the morphological similarity and biologic...

GATRsite: RNA-Ligand Binding Site Prediction Using Graph Attention Networks and Pretrained RNA Language Models.

Journal of chemical information and modeling
Identifying functional sites of RNA, particularly those where small molecules bind, is crucial for understanding related biological processes and advancing drug design. Small molecule therapies, compared to traditional protein-targeted therapies, hav...

Enhancing cardiac function assessment: Developing and validating a domain adaptive framework for automating the segmentation of echocardiogram videos.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: Accurate segmentation of echocardiographic images is essential for assessing cardiac function, particularly in calculating key metrics such as ejection fraction. However, challenges such as domain discrepancy, noisy data, anatomical varia...

Umami-Transformer: A deep learning framework for high-precision prediction and experimental validation of umami peptides.

Food chemistry
In food field, both identification of umami peptides and their sensory evaluation are limited by low efficiency of traditional methods and subjectivity of human-based assessments. To overcome these issues, Umami-Transformer was developed by integrati...

Effects of using deep learning to predict the geographic origin of barley genebank accessions on genome-environment association studies.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Genome-environment association (GEA) is an approach for identifying adaptive loci by combining genetic variation with environmental parameters, offering potential for improving crop resilience. However, its application to genebank accessions is limit...

MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach.

European radiology experimental
BACKGROUND: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and ...

Enhanced residual-attention deep neural network for disease classification in maize leaf images.

Scientific reports
Disease classification in maize plant is necessary for immediate treatment to enhance agricultural production and assure global food sustainability. Recent advancements in deep learning, specifically convolutional neural networks, have shown outstand...