MultiRoiMix: A Data Augmentation Method for PET/CT Multimodal Medical Images-Reference-Cited by-同舟云学术

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MultiRoiMix: A Data Augmentation Method for PET/CT Multimodal Medical Images

Published:2024-06 Issue:3 Volume:44 Page:366-374
ISSN:1609-0985
Container-title:Journal of Medical and Biological Engineering
language:en
Short-container-title:J. Med. Biol. Eng.

Author:

Wang Meng,Zhou Xinhong,Jin Mingming,Zhang Yi,Liu Liu,Huang Gang^ORCID

Funder

the Key Program of the National Natural Science Foundation of China

the Construction Project of Shanghai Key Laboratory of Molecular Imaging

the National Natural Science Foundation of China

the Natural Science Foundation of Shanghai

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s40846-024-00862-y.pdf

Reference16 articles.

1. Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 1–48. https://doi.org/10.1186/s40537-019-0197-0

2. Li, Z., Kamnitsas, K., & Glocker, B. (2020). Analyzing overfitting under class imbalance in neural networks for image segmentation. IEEE Transactions on Medical Imaging, 40, 1065–1077. https://doi.org/10.1109/TMI.2020.3046692

3. Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. (2020). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18, 203–211. https://doi.org/10.1038/s41592-020-01008-z

4. I Goodfellow J Pouget-Abadie M Mirza et al. Generative Adversarial Nets. Neural Information Processing Systems. MIT Press, 2014. https://doi.org/10.3156/JSOFT.29.5_177_2

5. Chaitanya, K., Karani, N., Baumgartner, C. F., Becker, A. S., Donati, O. F., & Konukoglu, E. (2020). Semi-supervised task-driven data augmentation for medical image segmentation. Medical image analysis, 68, 101934. https://doi.org/10.1016/j.media.2020.101934

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