Transfer Learning in Optical Microscopy
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Proceedings |
| Conference | Simulation and Synthesis in Medical Imaging |
| MU Faculty or unit | |
| Citation | |
| Web | https://2021.sashimi-workshop.org/ |
| Doi | http://dx.doi.org/10.1007/978-3-030-87592-3_8 |
| Keywords | Fluorescence microscopy; Phase-contrast microscopy; GAN; Image synthesis; Machine learning |
| Description | Image synthesis is nowadays a very rapidly evolving branch of deep learning. One of possible applications of image synthesis is an image-to-image translation. There is currently a lot of focus orientated to applications of image translation in medicine, mainly involving translation between different screening techniques. One of other possible use of image translation in medicine and biology is in the task of translation between various imaging techniques in modern microscopy. In this paper, we propose a novel method based on DenseNet architecture and we compare it with Pix2Pix model in the task of translation from images imaged using phase-contrast technique to fluorescence images with focus on usability for cell segmentation. |
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