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Інформація × Реєстраційний номер 2120U007866, Матеріали видань та локальних репозитаріїв Категорія Препринт Назва роботи End2end image analysis of single-cell gel electrophoresis Автор Hirna MariyaHirna Mariya Дата публікації 01-01-2020 Постачальник інформації Український католицький університет Першоджерело https://hdl.handle.net/20.500.14570/4516 Видання Опис Single-cell gel electrophoresis is the standard test used by biomedical researchers to analyze damage to the cell. Currently, this test is only done using standard image processing techniques, that skews the outputs, requires manual work and/or human supervision. Other problems with current solutions include poor usability, lack of flexibility, and high price for commercial applications. In this work, we create a deep learning-based end2end pipeline, that receives images from the test as an input, and produces damage metrics as an output. We have trained UNet with SE-ResNet50 encoder on the custom-created synthetic dataset, which achieves the dice coefficient of 76.8. We hope that the results of this work will become the base of the easy-to-use open-source application available for any researcher. Додано в НРАТ 2025-11-05 Закрити