Weighted MR images of 421 patients with nasopharyngeal cancer were obtained at the head and neck level, and the tumors in the images were assessed by two expert doctors. 346 patients' multimodal pictures and labels served as training sets, whereas the remaining 75 patients' multimodal images and labels served as independent test sets. Convolutional neural network (CNN) for modal multidimensional information fusion and multimodal multidimensional information fusion (MMMDF) was used. The three models' performance is compared, and the findings reveal that the multimodal multidimensional fusion model performs best, while the two-modal multidimensional information fusion model performs second. The single-modal multidimensional information fusion model has the poorest performance. In MR images of nasopharyngeal cancer, a convolutional network can precisely and efficiently segment tumors.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420592 | PMC |
| http://dx.doi.org/10.1155/2022/5061112 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Multi-source biological knowledge-guided hypergraph spatiotemporal subnetwork embedding for protein complex identification.
Brief Bioinform
November 2024
Information Science and Technology College, Dalian Maritime University, No.1 Linghai Road, 116026, Dalian, Liaoning, China.
Identifying biologically significant protein complexes from protein-protein interaction (PPI) networks and understanding their roles are essential for elucidating protein functions, life processes, and disease mechanisms. Current methods typically rely on static PPI networks and model PPI data as pairwise relationships, which presents several limitations. Firstly, static PPI networks do not adequately represent the scopes and temporal dynamics of protein interactions.
View Article and Find Full Text PDFA Multi-Source drug combination and Omnidirectional feature fusion approach for predicting Drug-Drug interaction events.
J Biomed Inform
January 2025
Northwest Normal University, College of Computer Science and Engineering, Lanzhou, China. Electronic address:
Background: In the medical context where polypharmacy is increasingly common, accurately predicting drug-drug interactions (DDIs) is necessary for enhancing clinical medication safety and personalized treatment. Despite progress in identifying potential DDIs, a deep understanding of the underlying mechanisms of DDIs remains limited, constraining the rapid development and clinical application of new drugs.
Methods: This study introduces a novel multimodal drug-drug interaction (MMDDI) model based on multi-source drug data and comprehensive feature fusion techniques, aiming to improve the accuracy and depth of DDI prediction.
Tuning surface hydrophilicity of a BiVO photoanode through interface engineering for efficient PEC water splitting.
RSC Adv
January 2025
School of Electronic Engineering, Guangxi Key Laboratory of Multidimensional Information Fusion for Intelligent Vehicles, Guangxi University of Science and Technology Liuzhou 545000 China
This study presents a novel approach to enhance photoelectrochemical (PEC) water oxidation by integrating cobalt phthalocyanine (CoPc) with bismuth vanadate (BVO) a direct solvothermal method. The as-prepared BVO@CoPc photoanode demonstrated a photocurrent density of 4.0 mA cm at 1.
View Article and Find Full Text PDFLightweight multidimensional feature enhancement algorithm LPS-YOLO for UAV remote sensing target detection.
Sci Rep
January 2025
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, School of Information Engineering, Minzu University of China, Beijing, 100081, China.
Detecting small targets in UAV remote sensing images is challenging for traditional lightweight methods due to difficulty in feature extraction and high background interference. We propose LPS-YOLO, which improves small target feature extraction while reducing computational complexity by replacing the Conv backbone with SPDConv to retain fine-grained features. LPS-YOLO introduces the SKAPP module for better feature fusion and incorporates the E-BiFPN and OFTP structures to efficiently preserve and transfer backbone information.
View Article and Find Full Text PDFMFF-Net: A Lightweight Multi-Frequency Network for Measuring Heart Rhythm from Facial Videos.
Sensors (Basel)
December 2024
College of Electrical Engineering, Sichuan University, Chengdu 610065, China.
Remote photo-plethysmography (rPPG) is a useful camera-based health motioning method that can measure the heart rhythm from facial videos. Many well-established deep learning models can provide highly accurate and robust results in measuring heart rate (HR) and heart rate variability (HRV). However, these methods are unable to effectively eliminate illumination variation and motion artifact disturbances, and their substantial computational resource requirements significantly limit their applicability in real-world scenarios.
View Article and Find Full Text PDFWant 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!