Roadmap on Deep Learning for Microscopy
Abstract
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.
Cite
@article{arxiv.2303.03793,
title = {Roadmap on Deep Learning for Microscopy},
author = {Giovanni Volpe and Carolina Wählby and Lei Tian and Michael Hecht and Artur Yakimovich and Kristina Monakhova and Laura Waller and Ivo F. Sbalzarini and Christopher A. Metzler and Mingyang Xie and Kevin Zhang and Isaac C. D. Lenton and Halina Rubinsztein-Dunlop and Daniel Brunner and Bijie Bai and Aydogan Ozcan and Daniel Midtvedt and Hao Wang and Nataša Sladoje and Joakim Lindblad and Jason T. Smith and Marien Ochoa and Margarida Barroso and Xavier Intes and Tong Qiu and Li-Yu Yu and Sixian You and Yongtao Liu and Maxim A. Ziatdinov and Sergei V. Kalinin and Arlo Sheridan and Uri Manor and Elias Nehme and Ofri Goldenberg and Yoav Shechtman and Henrik K. Moberg and Christoph Langhammer and Barbora Špačková and Saga Helgadottir and Benjamin Midtvedt and Aykut Argun and Tobias Thalheim and Frank Cichos and Stefano Bo and Lars Hubatsch and Jesus Pineda and Carlo Manzo and Harshith Bachimanchi and Erik Selander and Antoni Homs-Corbera and Martin Fränzl and Kevin de Haan and Yair Rivenson and Zofia Korczak and Caroline Beck Adiels and Mite Mijalkov and Dániel Veréb and Yu-Wei Chang and Joana B. Pereira and Damian Matuszewski and Gustaf Kylberg and Ida-Maria Sintorn and Juan C. Caicedo and Beth A Cimini and Muyinatu A. Lediju Bell and Bruno M. Saraiva and Guillaume Jacquemet and Ricardo Henriques and Wei Ouyang and Trang Le and Estibaliz Gómez-de-Mariscal and Daniel Sage and Arrate Muñoz-Barrutia and Ebba Josefson Lindqvist and Johanna Bergman},
journal= {arXiv preprint arXiv:2303.03793},
year = {2023}
}