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Synthetic data for unsupervised polyp segmentation

Moreu, Enric, McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2022) Synthetic data for unsupervised polyp segmentation. In: 29th Irish Conference on Artificial Intelligence and Cognitive Science, 9-10 Dec 2021, Dublin, Ireland.

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
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Computer Vision; Synthetic Data; Polyp Segmentation; Unsupervised Learning
Subjects:Computer Science > Artificial intelligence
Computer Science > Computer simulation
Computer Science > Machine learning
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings of The 29th Irish Conference on Artificial Intelligence and Cognitive Science 2021. CEUR Workshop Proceedings 3105. CEUR-WS.
Publisher:CEUR-WS
Official URL:https://ceur-ws.org/Vol-3105/paper25.pdf
Copyright Information:© 2021 The Authors (CC-BY-4.0)
Funders:European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 765140., Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 the European Regional Development Fund. P2, co-funded b
ID Code:26498
Deposited On:01 Dec 2021 16:36 by Enric Moreu . Last Modified 16 Jan 2023 16:06
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