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Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach.

H. Chu, L. la O. Arévalo, W. Tang, B. Ma, Y. Li, A. Biase, S. Both, J. Langendijk, P. van Ooijen, N. Sijtsema, and L. van Dijk. HECKTOR@MICCAI, volume 13626 of Lecture Notes in Computer Science, page 114-120. Springer, (2022)

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Yuzhao Ma

Characterization of higher order modes in optical fibersY. Ma. Uni Erlangen-Nürnberg, (2009)

Tsie Ma

Jingxiang Ma

Lan Ma

Ma Li

Other publications of authors with the same name

Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach.H. Chu, L. la O. Arévalo, W. Tang, B. Ma, Y. Li, A. Biase, S. Both, J. Langendijk, P. van Ooijen, N. Sijtsema and 1 other author(s). HECKTOR@MICCAI, volume 13626 of Lecture Notes in Computer Science, page 114-120. Springer, (2022)TransRP: Transformer-based PET/CT feature extraction incorporating clinical data for recurrence-free survival prediction in oropharyngeal cancer.B. Ma, J. Guo, L. van Dijk, P. van Ooijen, S. Both, and N. Sijtsema. MIDL, volume 227 of Proceedings of Machine Learning Research, page 1640-1654. PMLR, (2023)Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images.A. Biase, W. Tang, N. Sourlos, B. Ma, J. Guo, N. Sijtsema, and P. van Ooijen. HECKTOR@MICCAI, volume 13209 of Lecture Notes in Computer Science, page 109-120. Springer, (2021)Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer.B. Ma, J. Guo, A. Biase, N. Sourlos, W. Tang, P. van Ooijen, S. Both, and N. Sijtsema. HECKTOR@MICCAI, volume 13209 of Lecture Notes in Computer Science, page 308-317. Springer, (2021)Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method.X. Wang, Q. Li, Y. Zhao, Y. He, B. Ma, Z. Fu, and S. Li. NeuroImage, (2021)Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients.B. Ma, Y. Li, H. Chu, W. Tang, L. la O. Arévalo, J. Guo, P. van Ooijen, S. Both, J. Langendijk, L. van Dijk and 1 other author(s). HECKTOR@MICCAI, volume 13626 of Lecture Notes in Computer Science, page 240-254. Springer, (2022)Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer.A. Biase, B. Ma, J. Guo, L. van Dijk, J. Langendijk, S. Both, P. van Ooijen, and N. Sijtsema. Comput. Methods Programs Biomed., (2024)

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Disambiguation of "Ma, Baoqiang"

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Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach.

Please choose a person to relate this publication to

Yuzhao Ma

Tsie Ma

Jingxiang Ma

Lan Ma

Ma Li

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Ma, Baoqiang"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach.

Please choose a person to relate this publication to

Yuzhao Ma

Tsie Ma

Jingxiang Ma

Lan Ma

Ma Li

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach.