Transfer Learning in Optical Microscopy

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KOZLOVSKÝ Martin WIESNER David SVOBODA David

Rok publikování 2021
Druh Článek ve sborníku
Konference Simulation and Synthesis in Medical Imaging
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www https://2021.sashimi-workshop.org/
Doi http://dx.doi.org/10.1007/978-3-030-87592-3_8
Klíčová slova Fluorescence microscopy; Phase-contrast microscopy; GAN; Image synthesis; Machine learning
Popis 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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