Simple U-net Based Synthetic Polyp Image Generation: Polyp to Negative
and Negative to Polyp
- URL: http://arxiv.org/abs/2302.09835v1
- Date: Mon, 20 Feb 2023 08:47:44 GMT
- Title: Simple U-net Based Synthetic Polyp Image Generation: Polyp to Negative
and Negative to Polyp
- Authors: Hemin Ali Qadir, Ilangko Balasingham, Younghak Shin
- Abstract summary: Synthetic polyp generation is a good alternative to overcome the privacy problem of medical data.
We propose a deep learning-based polyp image generation framework that generates synthetic polyp images similar to real ones.
- Score: 5.943584566266871
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Synthetic polyp generation is a good alternative to overcome the privacy
problem of medical data and the lack of various polyp samples. In this study,
we propose a deep learning-based polyp image generation framework that
generates synthetic polyp images that are similar to real ones. We suggest a
framework that converts a given polyp image into a negative image (image
without a polyp) using a simple conditional GAN architecture and then converts
the negative image into a new-looking polyp image using the same network. In
addition, by using the controllable polyp masks, polyps with various
characteristics can be generated from one input condition. The generated polyp
images can be used directly as training images for polyp detection and
segmentation without additional labeling. To quantitatively assess the quality
of generated synthetic polyps, we use public polyp image and video datasets
combined with the generated synthetic images to examine the performance
improvement of several detection and segmentation models. Experimental results
show that we obtain performance gains when the generated polyp images are added
to the training set.
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