Tsung-Ying Ho
Orcid: 0000-0002-8468-2226
According to our database1,
Tsung-Ying Ho
authored at least 16 papers
between 2006 and 2022.
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Bibliography
2022
Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation.
CoRR, 2022
Thoracic Lymph Node Segmentation in CT Imaging via Lymph Node Station Stratification and Size Encoding.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022
2021
DeepTarget: Gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy.
Medical Image Anal., 2021
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study.
CoRR, 2021
Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT.
CoRR, 2021
2020
Classifying Neck Lymph Nodes of Head and Neck Squamous Cell Carcinoma in MRI Images with Radiomic Features.
J. Digit. Imaging, 2020
CoRR, 2020
Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification.
CoRR, 2020
Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search.
CoRR, 2020
Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-Based Gating Using 3D CT/PET Imaging in Radiotherapy.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020
Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020
Organ at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020
2019
CoRR, 2019
Deep Esophageal Clinical Target Volume Delineation Using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019
Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT Using Two-Stream Chained 3D Deep Network Fusion.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019
2006
A Study on Collaborative Design forManufacturing Industry in Taiwan: Current Status and Platform Requirements.
Int. J. Electron. Bus. Manag., 2006