Region-of-interest guided Supervoxel Inpainting for Self-supervision
This work addresses biomedical image segmentation by improving self-supervised learning, though it is incremental as it builds on existing inpainting methods with structural modifications.
The paper tackled the problem of enhancing self-supervised learning for biomedical image segmentation by proposing region-of-interest guided supervoxel inpainting, resulting in consistent performance improvements over supervised CNNs and conventional inpainting methods across different training set sizes.
Self-supervised learning has proven to be invaluable in making best use of all of the available data in biomedical image segmentation. One particularly simple and effective mechanism to achieve self-supervision is inpainting, the task of predicting arbitrary missing areas based on the rest of an image. In this work, we focus on image inpainting as the self-supervised proxy task, and propose two novel structural changes to further enhance the performance of a deep neural network. We guide the process of generating images to inpaint by using supervoxel-based masking instead of random masking, and also by focusing on the area to be segmented in the primary task, which we term as the region-of-interest. We postulate that these additions force the network to learn semantics that are more attuned to the primary task, and test our hypotheses on two applications: brain tumour and white matter hyperintensities segmentation. We empirically show that our proposed approach consistently outperforms both supervised CNNs, without any self-supervision, and conventional inpainting-based self-supervision methods on both large and small training set sizes.