Our mission
Machine Learning for Infection and Disease (MLID) group (aka Yakimovich group) aims to develop the latest Machine Learning and Computer Science methods to facilitate our understanding of Infection Biology and Disease Biology.
Latest preprints
Liou, N. S., De, T., Urbanski, A., Chieng, C., Kong, Q., David, A. L., … & Horsley, H. (2023). A clinical microscopy dataset to develop a deep learning diagnostic test for urinary tract infection. medRxiv, 2023-09.
Li, Rui, Gabriel della Maggiora, Vardan Andriasyan, Anthony Petkidis, Artsemi Yushkevich, Mikhail Kudryashev, and Artur Yakimovich. “Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model.” arXiv preprint arXiv:2306.02929 (2023).
Latest papers
Li, Rui, Mikhail Kudryashev, and Artur Yakimovich. “A weak-labelling and deep learning approach for in-focus object segmentation in 3D widefield microscopy.” (2022).
Li, Rui, et al. “Open-Source Biomedical Image Analysis Models: A Meta-Analysis and Continuous Survey.” Frontiers in Bioinformatics (2022): 76.
Galimov, Evgeniy, and Artur Yakimovich. “A tandem segmentation-classification approach for the localization of morphological predictors of C. elegans lifespan and motility.” Aging (Albany NY) 14.4 (2022): 1665.
