Yaosheng Lu

Orcid: 0009-0008-8819-7093

According to our database1, Yaosheng Lu authored at least 24 papers between 2008 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2025
PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images.
Medical Image Anal., 2025

Segment Anything Model for fetal head-pubic symphysis segmentation in intrapartum ultrasound image analysis.
Expert Syst. Appl., 2025

2024
PSFHSP-Net: an efficient lightweight network for identifying pubic symphysis-fetal head standard plane from intrapartum ultrasound images.
Medical Biol. Eng. Comput., October, 2024

Fetal Head and Pubic Symphysis Segmentation in Intrapartum Ultrasound Image Using a Dual-Path Boundary-Guided Residual Network.
IEEE J. Biomed. Health Informatics, August, 2024

Direction-guided and multi-scale feature screening for fetal head-pubic symphysis segmentation and angle of progression calculation.
Expert Syst. Appl., 2024

Dual-path multi-branch feature residual network for salient object detection.
Eng. Appl. Artif. Intell., 2024

RTSeg-net: A lightweight network for real-time segmentation of fetal head and pubic symphysis from intrapartum ultrasound images.
Comput. Biol. Medicine, 2024

ETCNN: An ensemble transformer-convolutional neural network for automatic analysis of fetal heart rate.
Biomed. Signal Process. Control., 2024

2023
Automated fetal heart rate analysis for baseline determination using EMAU-Net.
Inf. Sci., October, 2023

The segmentation effect of style transfer on fetal head ultrasound image: a study of multi-source data.
Medical Biol. Eng. Comput., May, 2023

Baseline/acceleration/deceleration determination of fetal heart rate signals using a novel ensemble LCResU-Net.
Expert Syst. Appl., May, 2023

A hybrid attentional guidance network for tumors segmentation of breast ultrasound images.
Int. J. Comput. Assist. Radiol. Surg., 2023

2022
Prediction of Delivery Mode from Fetal Heart Rate and Electronic Medical Records Using Machine Learning.
Proceedings of the Computing in Cardiology, 2022

2021
Imputation Method for Fetal Heart Rate Signal Evaluation Based on Optimal Transport Theory.
SN Comput. Sci., 2021

An attention-based CNN-BiLSTM hybrid neural network enhanced with features of discrete wavelet transformation for fetal acidosis classification.
Expert Syst. Appl., 2021

Corrigendum to "Cardiotocography signal abnormality classification using time-frequency features and ensemble cost-sensitive SVM classifier" [Comput. Biol. Med. 130 (2021) 104218].
Comput. Biol. Medicine, 2021

Cardiotocography signal abnormality classification using time-frequency features and Ensemble Cost-sensitive SVM classifier.
Comput. Biol. Medicine, 2021

2020
Automatic Angle of Progress Measurement of Intrapartum Transperineal Ultrasound Image with Deep Learning.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

In Silico Assessment of Genetic Variation in PITX2 Reveals the Molecular Mechanisms of Calcium-Mediated Cellular Triggered Activity in Atrial Fibrillation<sup>*</sup>.
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020

A Novel Method for Nonlinear Dynamics Analysis of Fetal Heart Rate in Fetal Distress Using Visibility Graph.
Proceedings of the BIBE 2020: The Fourth International Conference on Biological Information and Biomedical Engineering, 2020

2019
In Silico Screening of the Key Electrical Remodelling Targets in Atrial Fibrillation-Induced Sinoatrial Node Dysfunction.
Proceedings of the 46th Computing in Cardiology, 2019

In Silico Investigation of the CACNA1C N2091S Mutation in Timothy Syndrome.
Proceedings of the 46th Computing in Cardiology, 2019

PITX2 Overexpression Leads to Atrial Electrical Remodeling Linked to Atrial Fibrillation.
Proceedings of the 46th Computing in Cardiology, 2019

2008
A Comparative Study to Extract the Diaphragmatic Electromyogram Signal.
Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics, 2008


  Loading...