Background: Practical implementation of radiomics research faces significant data accessibility challenges due to privacy and ethical restrictions on multicenter data aggregation. Federated Learning (FL) provides a secure distributed framework that preserves data privacy through cryptographic techniques. Its adoption in radiomics is an emerging trend, enabling collaborative training without sharing sensitive imaging data. However, the inherently Non-IID data distribution across clients in FL often leads to class imbalance, which can substantially degrade global model performance. Purpose: To develop a privacy-preserving, multicenter collaborative CT-radiomics model for evaluating neoadjuvant... More >

In the era of increasingly scarce spectrum resources, electromagnetic spectrum (EMS) prediction has emerged as a critical means for enhancing spectrum utilization efficiency. However, most of the existing EMS methods primarily exploit low-dimensional features such as temporal, frequency, or spatial characteristics in an individual fashion, which limits their ability to fully capture the inherent complexity of spectrum dynamics. To improve the performance, this paper proposes a novel EMS prediction model, which involving three operations, namely multi-dimensional decoupling, feature fusion and temporal prediction. Firstly, for multi-dimensional decoupling operation, we propose a Multi-dimensi... More >