Synthetic data for unsupervised polyp segmentation

• URL: http://arxiv.org/abs/2202.08680v1
• Date: Thu, 17 Feb 2022 14:32:33 GMT
• Title: Synthetic data for unsupervised polyp segmentation
• Authors: Enric Moreu, Kevin McGuinness, Noel E. O'Connor
• Abstract summary: We produce realistic synthetic colon images using a combination of 3D technologies and generative adversarial networks. Our fully unsupervised method achieves promising results on five real polyp segmentation datasets. As a part of this study we release Synth-Colon, an entirely synthetic dataset that includes 20000 realistic colon images.
• Score: 16.320983705522423
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Deep learning has shown excellent performance in analysing medical images. However, datasets are difficult to obtain due privacy issues, standardization problems, and lack of annotations. We address these problems by producing realistic synthetic images using a combination of 3D technologies and generative adversarial networks. We use zero annotations from medical professionals in our pipeline. Our fully unsupervised method achieves promising results on five real polyp segmentation datasets. As a part of this study we release Synth-Colon, an entirely synthetic dataset that includes 20000 realistic colon images and additional details about depth and 3D geometry: https://enric1994.github.io/synth-colon

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