Results 71 to 80 of about 379 (188)

SpineMAE: A bone‐window self‐supervised and structure‐aware framework for 3D cervical vertebra segmentation and fracture classification

Journal of Applied Clinical Medical Physics, Volume 27, Issue 5, May 2026.
Abstract Introduction Accurate segmentation and classification of cervical spine fractures are essential for timely diagnosis and clinical decision‐making in trauma care. Existing deep learning approaches often require extensive manual annotations and struggle to maintain anatomical consistency across vertebral levels, limiting their reliability and ...
Qing Liang, Jingding Zhao, Fang Yang, Lin Shi, Yang Song, Xianjun Chen, Zewen Shi, Qingjiang Pang   +7 more
wiley   +1 more source

Performance analysis of concatenated Reed-Solomon and next generation polar codes for 6G communication systems. [PDF]

Sci Rep
Wondimu E, Annamalai P, Gared F.
europepmc   +1 more source

Performance Analysis of Sphere Packed Aided Differential Space-Time Spreading with Iterative Source-Channel Detection. [PDF]

Sensors (Basel), 2021
Khan HU, Minallah N, Masood A, Khalil A, Frnda J, Nedoma J.   +5 more
europepmc   +1 more source

Graph Neural Network in Raman Spectroscopy to Leverage the Performance and Interpretability of the Classification

Chemistry–Methods, Volume 6, Issue 5, May 2026.
Graphic neural networks are constructed for Raman‐based biomedical applications after transferring Raman spectra into graph representations, i.e., nodes and edges. The classification is observed to provide better robustness against disturbing spectral variations such as device‐to‐device differences.
Shuxia Guo, Sandra Baaba Frempong, Markus Salbreiter, Annette Wagenhaus, Petra Rösch, Jürgen Popp, Thomas Bocklitz   +6 more
wiley   +1 more source

DNA StairLoop: enabling high-fidelity data recovery and robust error correction in DNA-based data storage. [PDF]

Nat Commun
Yan Z, Qu G, Chen X, Zheng G, Wu H.
europepmc   +1 more source

Accelerating Multiparametric Quantitative MRI Using Self‐Supervised Scan‐Specific Implicit Neural Representation With Model Reinforcement

Magnetic Resonance in Medicine, Volume 95, Issue 5, Page 2963-2979, May 2026.
ABSTRACT Purpose To develop a self‐supervised scan‐specific deep learning framework for reconstructing accelerated multiparametric quantitative MRI (qMRI). Methods We propose REFINE‐MORE (REference‐Free Implicit NEural representation with MOdel REinforcement), combining an implicit neural representation (INR) architecture with a model reinforcement ...
Ruimin Feng, Albert Jang, Xingxin He, Fang Liu   +3 more
wiley   +1 more source

Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data. [PDF]

IEEE Rev Biomed Eng
Zhuang L, Park SH, Skates SJ, Prosper AE, Aberle DR, Hsu W.   +5 more
europepmc   +1 more source

pLM‐Repeat: Exploiting the sequence representations of protein language models for sensitive repeat detection

Protein Science, Volume 35, Issue 5, May 2026.
Abstract Duplication is an essential mechanism of molecular evolution, which operates across biological scales, from whole genomes to single basepairs. Its study is central to understanding protein evolution, but the detection of duplication events often becomes challenging over evolutionary time, due to the accumulating sequence divergence.
Kaiyu Qiu, Andrei N. Lupas, Stanislaw Dunin‐Horkawicz   +2 more
wiley   +1 more source

Attribute knowledge and KBGAT for predicting the accuracy of the harmonized system code for classifying import and export commodities. [PDF]

Sci Rep
Qi L, Zhang Q, Lin X, Zhang J, Liao M.
europepmc   +1 more source

ConforFold recovers alternative protein conformations beyond MSA subsampling

Protein Science, Volume 35, Issue 5, May 2026.
Abstract Conformational changes underlie many aspects of protein function, yet current structure prediction tools remain limited in their ability to systematically sample structural ensembles. Here, we present ConforPSSP and ConforFold, a combined framework that integrates secondary‐structure sampling into a deep learning‐based prediction to recover ...
Raulia Syrlybaeva, Eva‐Maria Strauch
wiley   +1 more source