Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1097/00001888-199405000-00028 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Optimized sequential model for superior classification of plant disease.
Sci Rep
January 2025
D. Y. Patil Agriculture and Technical University, Talsande, Maharashtra, India.
Indian agriculture is vital sector in the country's economy, providing employment and sustenance to millions of farmers. However, Plant diseases are a serious risk to crop yields and farmers' livelihoods. Traditional plant disease diagnosis methods rely heavily on human expertise, which can lead to inaccuracies due to the invisible nature of early disease symptoms and the labor-intensive process, making them inefficient for large-scale agricultural management.
View Article and Find Full Text PDFSimplified self-supervised learning for hybrid propagation graph-based recommendation.
Neural Netw
January 2025
School of Big Data & Software Engineering, Chongqing University, Chongqing, 401331, China. Electronic address:
Recent progress in Graph Convolutional Networks (GCNs) has facilitated their extensive application in recommendation, yielding notable performance gains. Nevertheless, existing GCN-based recommendation approaches are confronted with several challenges: (1) how to effectively leverage multi-order graph connectivity to derive meaningful node embeddings; (2) faced with sparse raw data, how to augment supervision signals without relying on auxiliary information; (3) given that GCNs necessitate the aggregation of neighborhood nodes, and the sparsity of these nodes can exacerbate the impact of noise data, how to mitigate the noise problem inherent in the raw data. For tackling aforementioned challenges, we devise a new hybrid propagation GCN-based method named S3HGN, incorporating a simplified self-supervised learning paradigm for recommendation.
View Article and Find Full Text PDFMotion Hologram: Jointly optimized hologram generation and motion planning for photorealistic 3D displays via reinforcement learning.
Sci Adv
January 2025
State Key Lab of Advanced Optical Communication Systems and Networks, Shanghai Jiao Tong University, Shanghai 200240, China.
Holography is capable of rendering three-dimensional scenes with full-depth control and delivering transformative experiences across numerous domains, including virtual and augmented reality, education, and communication. However, traditional holography presents 3D scenes with unnatural defocus and severe speckles due to the limited space bandwidth product of the spatial light modulator (SLM). Here, we introduce Motion Hologram, a holographic technique that accurately portrays photorealistic and speckle-free 3D scenes.
View Article and Find Full Text PDFscHNTL: single-cell RNA-seq data clustering augmented by high-order neighbors and triplet loss.
Bioinformatics
January 2025
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Sichuan 611756, China.
Motivation: The rapid development of single-cell RNA sequencing (scRNA-seq) has significantly advanced biomedical research. Clustering analysis, crucial for scRNA-seq data, faces challenges including data sparsity, high dimensionality, and variable gene expressions. Better low-dimensional embeddings for these complex data should maintain intrinsic information while making similar data close and dissimilar data distant.
View Article and Find Full Text PDFGenerative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!