CellGAN: Generative Adversarial Networks for Cellular Microscopy Image Recognition with Integrated Feature Completion Mechanism
In response to the challenges of high noise, high adhesion, and a low signal-to-noise ratio in microscopic cell images, as well as the difficulty of existing deep learning models such as UNet, ResUNet, and SwinUNet in segmenting images with clear boundaries and high-resolution, this study proposes a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/14/6266 |