The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Unpaired Multi-Modal Segmentation via Knowledge Distillation
Open accessAbstract
Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities. We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI, and only employ modality-specific internal normalization layers which compute respective statistics. To effectively train such a highly compact model, we introduce a novel loss term inspired by knowledge distillation, by explicitly constraining the KL-divergence of our derived prediction distributions between modalities. We have extensively validated our approach on two multi-class segmentation problems: i) cardiac structure segmentation, and ii) abdominal organ segmentation. Different network settings, i.e., 2D dilated network and 3D U-net, are utilized to investigate our method's general efficacy. Experimental results on both tasks demonstrate that our novel multi-modal learning scheme consistently outperforms single-modal training and previous multi-modal approaches.
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Cites
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498
Self-citationAutomatic Segmentation of MR Brain Images With a Convolutional Neural Network
Moeskops, P., Viergever, M. A., Mendrik, A. M., de Vries, L. S., Benders, M. J. N. L., & Isgum, I. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2548501 · Cited by 861
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.061 · Cited by 817
Fully convolutional networks for multi-modality isointense infant brain image segmentation
Nie, D., Wang, L., Gao, Y., & Shen, D. (2016) · 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493515 · Cited by 304 · cited 4×
“We compare the performance of different multi-modal learning methods, including two state-of-the-art approaches [9], [15].”
BackgroundMethodInfluentialLifelong Learning via Progressive Distillation and Retrospection
Hou, S., Pan, X., Loy, C. C., Wang, Z., & Lin, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_27 · Cited by 190
Multi-modal learning from unpaired images: application to multi-organ segmentation in CT and MRI
Valindria, V. V., Pawlowski, N., Rajchl, M., Lavdas, I., Aboagye, E. O., Rockall, A. G., Rueckert, D., & Glocker, B. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00066 · Cited by 146
Self-citationScalable Multimodal Convolutional Networks for Brain Tumour Segmentation
Fidon, L., Li, W., Garcia-Peraza-Herrera, L. C., Ekanayake, J., Kitchen, N., Ourselin, S., & Vercauteren, T. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66179-7_33 · Cited by 60
Cited by
Knowledge Distillation: A Survey
Gou, J., Yu, B., Maybank, S. J., & Tao, D. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01453-z · Cited by 3,931
Medical image segmentation using deep learning: A survey
Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022) · IET Image Processing · doi:10.1049/ipr2.12419 · Cited by 892
FusionM4Net: a multi-stage multi-modal learning algorithm for multi-label skin lesion classification
Tang, P., Yan, X., Nan, Y., Xiang, S., Krammer, S., & Lasser, T. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102307 · Cited by 131
Metadata
- Authors
- Dou, Q., Liu, Q., Heng, P. A., & Glocker, B.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2020-07
- Volume
- 39
- Issue
- 7
- Pages
- 2415-2425
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W3005193778
- Cited by
- 212
- References
- 10
- FWCI
- 11.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Dou, Q., Liu, Q., Heng, P. A., & Glocker, B. (2020). Unpaired Multi-Modal Segmentation via Knowledge Distillation. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2019.2963882
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