Results 61 to 70 of about 822,037 (305)

Predicting Chronicity in Children and Adolescents With Newly Diagnosed Immune Thrombocytopenia at the Timepoint of Diagnosis Using Machine Learning‐Based Approaches

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Objectives To identify predictors of chronic ITP (cITP) and to develop a model based on several machine learning (ML) methods to estimate the individual risk of chronicity at the timepoint of diagnosis. Methods We analyzed a longitudinal cohort of 944 children enrolled in the Intercontinental Cooperative immune thrombocytopenia (ITP) Study ...
Severin Kasser, Alice Bizeul, Meera Chitlur, Hugo Donato, Jelena Roganovic, Julia E. Vogt, Thomas Kühne   +6 more
wiley   +1 more source

Psychological Safety Among Interprofessional Pediatric Oncology Teams in Germany: A Nationwide Survey

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Psychological safety (PS) is essential for teamwork, communication, and patient safety in complex healthcare environments. In pediatric oncology, interprofessional collaboration occurs under high emotional and organizational demands. Low PS may increase stress, burnout, and adverse events.
Alexandros Rahn, Anna‐Lena Herbach, Christian Bock, Katharina Fleig, Urs Mücke   +4 more
wiley   +1 more source

Toward Efficient Object Detection in Aerial Images Using Extreme Scale Metric Learning

IEEE Access, 2021
In aerial image object detection, how to efficiently detect different size objects in input images of different scales and obtain a unified multi-scale representation of the object is an important issue.
Ren Jin, Junning Lv, Bin Li, Jianchuan Ye, Defu Lin   +4 more
doaj   +1 more source

Metric Learning Based Positioning

2025 IEEE Wireless Communications and Networking Conference (WCNC)
Peer ...
Al-Tous Hanan, Tirkkonen Olav, Srinivasan Ashvin, Kokkonen Santeri, Li Xinze   +4 more
openaire   +2 more sources

Predicting the Future Burden of Renal Replacement Therapy in Türkiye Using National Registry Data and Comparative Modeling Approaches

Therapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Background Chronic kidney disease is a growing public health problem worldwide, and the number of patients requiring renal replacement therapy is steadily increasing. Türkiye has experienced a similar rise in both the incidence and prevalence of renal replacement therapy over the past decades; however, national‐level projections of future ...
Arzu Akgül, Neriman Sıla Koç, Berrak Itır Aylı   +2 more
wiley   +1 more source

Person Re-Identification Based on Multi-Parts of Local Feature Network

IEEE Access, 2019
Person re-identification has become increasing popular because of its widely application in computer vision. In this paper, we propose a novel, simple and efficient person re-id network called MPLFN.
Zimian Wei, Wenjing Yang, Wanrong Huang, Huadong Dai, Dongsheng Li   +4 more
doaj   +1 more source

Disordered but rhythmic—the role of intrinsic protein disorder in eukaryotic circadian timing

FEBS Letters, EarlyView.
Unstructured domains known as intrinsically disordered regions (IDRs) are present in nearly every part of the eukaryotic core circadian oscillator. IDRs enable many diverse inter‐ and intramolecular interactions that support clock function. IDR conformations are highly tunable by post‐translational modifications and environmental conditions, which ...
Emery T. Usher, Jacqueline F. Pelham
wiley   +1 more source

Robust Person Re-Identification Through the Combination of Metric Learning and Late Fusion Techniques

Vietnam Journal of Computer Science, 2021
Fusion techniques with the aim to leverage the discriminative power of different appearance features for person representation have been widely applied in person re-identification.
Hong-Quan Nguyen, Thuy-Binh Nguyen, Thi-Lan Le   +2 more
doaj   +1 more source

Recursive Router Metrics Prediction Using Machine Learning-Based Node Modeling for Network Digital Replica [PDF]

, 2023
Kyota Hattori, Tomohiro Korikawa, Chikako Takasaki, Hidenari Oowada   +3 more
openalex   +1 more source

Introspective Deep Metric Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods focus on learning a discriminative embedding to describe the semantic features of images, which ignore the existence of uncertainty in each image resulting from noise or semantic ambiguity ...
Chengkun Wang, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu   +4 more
openaire   +3 more sources