Tao Peng
Orcid: 0000-0003-0848-7901Affiliations:
- Soochow University, School of Future Science and Engineering, Suzhou, China
- Hong Kong Polytechnic University, Department of Health Technology and Informatics, Hong Kong
- UT Southwestern Medical Center, Department of Radiation Oncology, Dallas, TX, USA
According to our database1,
Tao Peng
authored at least 18 papers
between 2019 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2024
Organ boundary delineation for automated diagnosis from multi-center using ultrasound images.
Expert Syst. Appl., March, 2024
A multi-center study of ultrasound images using a fully automated segmentation architecture.
Pattern Recognit., January, 2024
2023
Coarse-to-fine tuning knowledgeable system for boundary delineation in medical images.
Appl. Intell., December, 2023
A mathematical and neural network-based hybrid technique for detecting the prostate contour from medical image data.
Biomed. Signal Process. Control., September, 2023
Automatic coarse-to-refinement-based ultrasound prostate segmentation using optimal polyline segment tracking method and deep learning.
Appl. Intell., September, 2023
A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets.
J. Digit. Imaging, August, 2023
Ultrasound Prostate Segmentation Using Adaptive Selection Principal Curve and Smooth Mathematical Model.
J. Digit. Imaging, June, 2023
Delineation of Prostate Boundary from Medical Images via a Mathematical Formula-Based Hybrid Algorithm.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2023, 2023
Interactive Ultrasound Prostate Cancer Segmentation using Deep Learning with Principal Curve-based Fine-tuning.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023
2022
H-ProMed: Ultrasound image segmentation based on the evolutionary neural network and an improved principal curve.
Pattern Recognit., 2022
H-SegMed: A Hybrid Method for Prostate Segmentation in TRUS Images via Improved Closed Principal Curve and Improved Enhanced Machine Learning.
Int. J. Comput. Vis., 2022
Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers.
CoRR, 2022
H-ProSeg: Hybrid ultrasound prostate segmentation based on explainability-guided mathematical model.
Comput. Methods Programs Biomed., 2022
Recurrence-Free Survival Prediction Under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2022
Improving the Detection of The Prostrate in Ultrasound Images Using Machine Learning Based Image Processing.
Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022
Explainability-guided Mathematical Model-Based Segmentation of Transrectal Ultrasound Images for Prostate Brachytherapy.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022
2020
2019
Segmentation of Lung in Chest Radiographs Using Hull and Closed Polygonal Line Method.
IEEE Access, 2019