Region-of-interest guided supervoxel inpainting for self-supervision
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Submitted manuscript, 322 KB, PDF document
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. Our method can be regarded as an efficient addition to self-supervision, where 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.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings |
Editors | Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz |
Publisher | Springer VS |
Publication date | 2020 |
Pages | 500-509 |
ISBN (Print) | 9783030597092 |
DOIs | |
Publication status | Published - 2020 |
Event | 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru Duration: 4 Oct 2020 → 8 Oct 2020 |
Conference
Conference | 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 |
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Land | Peru |
By | Lima |
Periode | 04/10/2020 → 08/10/2020 |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12261 LNCS |
ISSN | 0302-9743 |
- Brain tumor, Deep learning, Inpainting, Segmentation, Self-supervision, White matter hyperintensities
Research areas
ID: 250446239