Chen Shen
Orcid: 0000-0001-8284-9048Affiliations:
- Nagoya University, Graduate School of Informatics, Nagoya, Japan
According to our database1,
Chen Shen
authored at least 15 papers
between 2018 and 2024.
Collaborative distances:
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Bibliography
2024
Anatomical attention can help to segment the dilated pancreatic duct in abdominal CT.
Int. J. Comput. Assist. Radiol. Surg., April, 2024
2023
ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops, 2023
2022
A cascaded fully convolutional network framework for dilated pancreatic duct segmentation.
Int. J. Comput. Assist. Radiol. Surg., 2022
Proceedings of the Medical Imaging 2022: Image Processing, 2022
Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning.
Proceedings of the Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health, 2022
2021
Extraction of lung and lesion regions from COVID-19 CT volumes using 3D fully convolutional networks.
Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021
Proceedings of the Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning, 2021
Proceedings of the Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning, 2021
2020
Spatial information-embedded fully convolutional networks for multi-organ segmentation with improved data augmentation and instance normalization.
Proceedings of the Medical Imaging 2020: Image Processing, 2020
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning.
Proceedings of the Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, 2020
Usefulness of fine-tuning for deep learning based multi-organ regions segmentation method from non-contrast CT volumes using small training dataset.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
2019
Proceedings of the Medical Imaging 2019: Image Processing, 2019
2018
On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks.
CoRR, 2018
A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018