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arXiv · 2406.00947

Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation

Fei Gao·Siwen Wang·Fandong Zhang·Hong-Yu Zhou·Yizhou Wang·Churan Wang·Gang Yu·Yizhou Yu
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Abstract

Medical image analysis suffers from a shortage of data, whether annotated or not. This becomes even more pronounced when it comes to 3D medical images. Self-Supervised Learning (SSL) can partially ease this situation by using unlabeled data. However, most existing SSL methods can only make use of data in a single dimensionality (e.g. 2D or 3D), and are incapable of enlarging the training dataset by using data with differing dimensionalities jointly. In this paper, we propose a new cross-dimensional SSL framework based on a pseudo-3D transformation (CDSSL-P3D), that can leverage both 2D and 3D data for joint pre-training. Specifically, we introduce an image transformation based on the im2col algorithm, which converts 2D images into a format consistent with 3D data. This transformation enables seamless integration of 2D and 3D data, and facilitates cross-dimensional self-supervised learning for 3D medical image analysis. We run extensive experiments on 13 downstream tasks, including 2D and 3D classification and segmentation. The results indicate that our CDSSL-P3D achieves superior performance, outperforming other advanced SSL methods.

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Fei Gao, Siwen Wang, Fandong Zhang, Hong-Yu Zhou, Yizhou Wang, Churan Wang, Gang Yu, Yizhou Yu. 2024-06-03. Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation. https://arxiv.org/abs/2406.00947

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