Authors: Giovanni Volpe, Carolina Wählby, Lei Tian, Michael Hecht, Artur Yakimovich, Kristina Monakhova, Laura Waller, Ivo F Sbalzarini, Christopher A Metzler, Mingyang Xie, Kevin Zhang, Isaac CD Lenton, Halina Rubinsztein-Dunlop, Daniel Brunner, Bijie Bai, Aydogan Ozcan, Daniel Midtvedt, Hao Wang, Nataša Sladoje, Joakim Lindblad, Jason T Smith, Marien Ochoa, Margarida Barroso, Xavier Intes, Tong Qiu, Li-Yu Yu, Sixian You, Yongtao Liu, Maxim A Ziatdinov, Sergei V Kalinin, Arlo Sheridan, Uri Manor, Elias Nehme, Ofri Goldenberg, Yoav Shechtman, Henrik K Moberg, Christoph Langhammer, Barbora Špačková, Saga Helgadottir, Benjamin Midtvedt, Aykut Argun, Tobias Thalheim, Frank Cichos, Stefano Bo, Lars Hubatsch, Jesus Pineda, Carlo Manzo, Harshith Bachimanchi, Erik Selander, Antoni Homs-Corbera, Martin Fränzl, Kevin de Haan, Yair Rivenson, Zofia Korczak, Caroline Beck Adiels, Mite Mijalkov, Dániel Veréb, Yu-Wei Chang, Joana B Pereira, Damian Matuszewski, Gustaf Kylberg, Ida-Maria Sintorn, Juan C Caicedo, Beth A Cimini, Muyinatu A Lediju Bell, Bruno M Saraiva, Guillaume Jacquemet, Ricardo Henriques, Wei Ouyang, Trang Le, Estibaliz Gómez-de-Mariscal, Daniel Sage, Arrate Muñoz-Barrutia, Ebba Josefson Lindqvist, Johanna Bergman
Technologies: BioImage Model Zoo, CARE and ZeroCostDL4Mic
Preprint published in arXiv, January 2023
Publisher: ArXiv
The manuscript "Roadmap on Deep Learning for Microscopy" presents a comprehensive overview of the latest developments and possibilities of machine learning in microscopy. It covers various aspects of machine learning applications, including image quality improvement, object detection, segmentation, classification, and tracking, as well as efficient merging of information from multiple imaging modalities. The manuscript also discusses challenges and advances in these areas and their potential impact on scientific discovery. It introduces the concept of plug-and-play optimization for computational imaging reconstruction and proposes solutions to address challenges in this area. The manuscript also mentions the application of machine learning techniques to optical tweezers simulations and quantitative microscopy. The manuscript concludes by emphasizing the importance of these advancements for achieving specific quantitative imaging goals.