3D Manifold Topology Based Medical Image Data Augmentation

Jisui Huang, Na Lei
Proceedings of The 14th Asian Conference on Machine Learning, PMLR 189:499-514, 2023.

Abstract

Data augmentation is an effective and universal technique for improving the generalization performance of deep neural networks. Current data augmentation implementations usually involve geometric and photometric transformations. However, none of them considers the topological information in images, which is an important global invariant of the three-dimensional manifold. In our implementation, we design a novel method that finds the generator of the first homology group, i.e. closed loops cannot shrink to a point, of 3D image and erases the bounding box of a random loop. To the best of our knowledge, it is the first time that data augmentation based on the first homology group of the three-dimensional image is applied in medical image augmentation. Our numerical experiments demonstrate that the proposed approach outperforms the state-of-the-art method.

Cite this Paper

BibTeX
@InProceedings{pmlr-v189-huang23a, title = {3D Manifold Topology Based Medical Image Data Augmentation}, author = {Huang, Jisui and Lei, Na}, booktitle = {Proceedings of The 14th Asian Conference on Machine Learning}, pages = {499--514}, year = {2023}, editor = {Khan, Emtiyaz and Gonen, Mehmet}, volume = {189}, series = {Proceedings of Machine Learning Research}, month = {12--14 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v189/huang23a/huang23a.pdf}, url = {https://proceedings.mlr.press/v189/huang23a.html}, abstract = {Data augmentation is an effective and universal technique for improving the generalization performance of deep neural networks. Current data augmentation implementations usually involve geometric and photometric transformations. However, none of them considers the topological information in images, which is an important global invariant of the three-dimensional manifold. In our implementation, we design a novel method that finds the generator of the first homology group, i.e. closed loops cannot shrink to a point, of 3D image and erases the bounding box of a random loop. To the best of our knowledge, it is the first time that data augmentation based on the first homology group of the three-dimensional image is applied in medical image augmentation. Our numerical experiments demonstrate that the proposed approach outperforms the state-of-the-art method.} }
Endnote
%0 Conference Paper %T 3D Manifold Topology Based Medical Image Data Augmentation %A Jisui Huang %A Na Lei %B Proceedings of The 14th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Emtiyaz Khan %E Mehmet Gonen %F pmlr-v189-huang23a %I PMLR %P 499--514 %U https://proceedings.mlr.press/v189/huang23a.html %V 189 %X Data augmentation is an effective and universal technique for improving the generalization performance of deep neural networks. Current data augmentation implementations usually involve geometric and photometric transformations. However, none of them considers the topological information in images, which is an important global invariant of the three-dimensional manifold. In our implementation, we design a novel method that finds the generator of the first homology group, i.e. closed loops cannot shrink to a point, of 3D image and erases the bounding box of a random loop. To the best of our knowledge, it is the first time that data augmentation based on the first homology group of the three-dimensional image is applied in medical image augmentation. Our numerical experiments demonstrate that the proposed approach outperforms the state-of-the-art method.
APA
Huang, J. & Lei, N.. (2023). 3D Manifold Topology Based Medical Image Data Augmentation. Proceedings of The 14th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 189:499-514 Available from https://proceedings.mlr.press/v189/huang23a.html.

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