mlsanityData to 7 Oct 2026

Synthetic data for unsupervised polyp segmentation

arXiv 2202.08680 ↗First posted 17 Feb 2022eess.IV cs.CVcited by 2

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Abstract (arXiv)

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

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 17 Feb 2022.

Builds on

13 of 23 references.

References · 13

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  4. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 588
  5. 2020PraNet: Parallel Reverse Attention Network for Polyp Segmentationcited by 460
  6. 2019SinGAN: Learning a Generative Model from a Single Natural Imagecited by 348
  7. 2016A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Imagescited by 220
  8. 2019Cascaded Partial Decoder for Fast and Accurate Salient Object Detectioncited by 216
  9. 2019Learning from Synthetic Data for Crowd Counting in the Wildcited by 148
  10. 2019HarDNet: A Low Memory Traffic Networkcited by 76
  11. 2021HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPScited by 71
  12. 2017Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Trainingcited by 57
1 more
  1. 2021SinGAN-Seg: Synthetic training data generation for medical image segmentationcited by 43

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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023Joint one-sided synthetic unpaired image translation and segmentation for colorectal cancer preventioncited by 2
  2. 2023Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Datacited by 1