The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.
Models Genesis
Open accessAbstract
Transfer learning from natural image to medical image has been established as one of the most practical paradigms in deep learning for medical image analysis. To fit this paradigm, however, 3D imaging tasks in the most prominent imaging modalities (e.g., CT and MRI) have to be reformulated and solved in 2D, losing rich 3D anatomical information, thereby inevitably compromising its performance. To overcome this limitation, we have built a set of models, called Generic Autodidactic Models, nicknamed Models Genesis, because they are created ex nihilo (with no manual labeling), self-taught (learnt by self-supervision), and generic (served as source models for generating application-specific target models). Our extensive experiments demonstrate that our Models Genesis significantly outperform learning from scratch and existing pre-trained 3D models in all five target 3D applications covering both segmentation and classification. More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D/2.5D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of Models Genesis for 3D medical imaging. This performance is attributed to our unified self-supervised learning framework, built on a simple yet powerful observation: the sophisticated and recurrent anatomy in medical images can serve as strong yet free supervision signals for deep models to learn common anatomical representation automatically via self-supervision. As open science, all codes and pre-trained Models Genesis are available at https://github.com/MrGiovanni/ModelsGenesis.
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Cited by
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Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102680 · Cited by 1,412
The Liver Tumor Segmentation Benchmark (LiTS)
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2019) · arXiv · doi:10.48550/arxiv.1901.04056 · Cited by 1,412
Recent advances and clinical applications of deep learning in medical image analysis
Chen, X., Wang, X., Zhang, K., Fung, K.-M., Thai, T. C., Moore, K., Mannel, R. S., Liu, H., Zheng, B., & Qiu, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102444 · Cited by 1,080
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Tang, Y., Yang, D., Li, W., Roth, H. R., Landman, B., Xu, D., Nath, V., & Hatamizadeh, A. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02007 · Cited by 849
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Liu, J., Zhang, Y., Chen, J.-N., Xiao, J., Lu, Y., Landman, B. A., Yuan, Y., Yuille, A., Tang, Y., & Zhou, Z. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01934 · Cited by 333
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Metadata
- Authors
- Zhou, Z., Sodha, V., Pang, J., Gotway, M. B., & Liang, J.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 67
- Pages
- 101840
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W3016836174
- Cited by
- 328
- References
- 110
- FWCI
- 12.22
- Type
- journal article
- Language
- en
- Open access
- bronze
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