Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.hrthm.2025.03.1984DOI Listing

Publication Analysis

Top Keywords

rethinking ablation
4
ablation success
4
success integrating
4
integrating burden
4
burden time-to-event
4
time-to-event analysis
4
analysis burden-survival
4
burden-survival curve
4
rethinking
1
success
1

Similar Publications

Federated semi-supervised learning (FSSL) has recently emerged as a promising approach for enhancing the performance of federated learning (FL) using ubiquitous unlabeled data. However, this approach encounters challenges when learning a global model using both fully labeled and fully unlabeled clients. Previous works overlook the dissimilarities between labeled and unlabeled clients, predominantly using shared parameters for local training across these two types of clients, thereby inducing intertask interference during local training.

View Article and Find Full Text PDF

Background & Aims: For patients with single small (≤3 cm) hepatocellular carcinoma ablation is the first-line treatment, although a high rate of recurrence has been reported. The aim was to compare videolaparoscopic liver resection (laparoscopic resection group) percutaneous thermoablation (ablation group) in terms of overall survival, recurrence-free survival and early recurrence in a real-life national scenario.

Methods: The study is a retrospective collection with subsequent survival analysis.

View Article and Find Full Text PDF

Complex pest and disease features appearing during the growth of wheat crops are difficult to capture and can seriously affect the normal growth of wheat crops. The existing methods ignore the full pre-interaction of deep and shallow features, which largely affects the accuracy of identification. To address the above problems and needs, we rethink the feature representation and attention mechanism in intelligent recognition of wheat leaf diseases and pests, and propose a representation and recognition network (RReNet) based on the feature attention mechanism.

View Article and Find Full Text PDF