SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Data

• URL: http://arxiv.org/abs/2201.05905v1
• Date: Sat, 15 Jan 2022 18:42:38 GMT
• Title: SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Data
• Authors: Minh Tran, Loi Ly, Binh-Son Hua, Ngan Le
• Abstract summary: This work extends capsule networks for volumetric medical image segmentation with self-supervised learning. Our 3D capsule network with self-supervised pre-training considerably outperforms previous capsule networks and 3D-UNets.
• Score: 10.371128893952537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Capsule network is a recent new deep network architecture that has been applied successfully for medical image segmentation tasks. This work extends capsule networks for volumetric medical image segmentation with self-supervised learning. To improve on the problem of weight initialization compared to previous capsule networks, we leverage self-supervised learning for capsule networks pre-training, where our pretext-task is optimized by self-reconstruction. Our capsule network, SS-3DCapsNet, has a UNet-based architecture with a 3D Capsule encoder and 3D CNNs decoder. Our experiments on multiple datasets including iSeg-2017, Hippocampus, and Cardiac demonstrate that our 3D capsule network with self-supervised pre-training considerably outperforms previous capsule networks and 3D-UNets.

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