Roadmap on Deep Learning for Microscopy

Autor: Volpe, Giovanni, Wählby, Carolina, Tian, Lei, Hecht, Michael, Yakimovich, Artur, Monakhova, Kristina, Waller, Laura, Sbalzarini, Ivo F., Metzler, Christopher A., Xie, Mingyang, Zhang, Kevin, Lenton, Isaac C. D., Rubinsztein-Dunlop, Halina, Brunner, Daniel, Bai, Bijie, Ozcan, Aydogan, Midtvedt, Daniel, Wang, Hao, Sladoje, Nataša, Lindblad, Joakim, Smith, Jason T., Ochoa, Marien, Barroso, Margarida, Intes, Xavier, Qiu, Tong, Yu, Li-Yu, You, Sixian, Liu, Yongtao, Ziatdinov, Maxim A., Kalinin, Sergei V., Sheridan, Arlo, Manor, Uri, Nehme, Elias, Goldenberg, Ofri, Shechtman, Yoav, Moberg, Henrik K., Langhammer, Christoph, Špačková, Barbora, Helgadottir, Saga, Midtvedt, Benjamin, Argun, Aykut, Thalheim, Tobias, Cichos, Frank, Bo, Stefano, Hubatsch, Lars, Pineda, Jesus, Manzo, Carlo, Bachimanchi, Harshith, Selander, Erik, Homs-Corbera, Antoni, Fränzl, Martin, de Haan, Kevin, Rivenson, Yair, Korczak, Zofia, Adiels, Caroline Beck, Mijalkov, Mite, Veréb, Dániel, Chang, Yu-Wei, Pereira, Joana B., Matuszewski, Damian, Kylberg, Gustaf, Sintorn, Ida-Maria, Caicedo, Juan C., Cimini, Beth A, Bell, Muyinatu A. Lediju, Saraiva, Bruno M., Jacquemet, Guillaume, Henriques, Ricardo, Ouyang, Wei, Le, Trang, Gómez-de-Mariscal, Estibaliz, Sage, Daniel, Muñoz-Barrutia, Arrate, Lindqvist, Ebba Josefson, Bergman, Johanna

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 netw

Externí odkaz: http://arxiv.org/abs/2303.03793

A phase Ib randomized multicenter trial of isolated hepatic perfusion in combination with ipilimumab and nivolumab for uveal melanoma metastases (SCANDIUM II trial)

Autor: Olofsson Bagge, R., Nelson, A., Shafazand, A., Cahlin, C., Carneiro, A., Helgadottir, H., Levin, M., Rizell, M., Ullenhag, G., Wirén, S., Lindnér, P., Nilsson, J.A., Ny, L.

Publikováno v: In ESMO Open July 2024 9(7)

Extracting quantitative biological information from brightfield cell images using deep learning

Autor: Helgadottir, Saga, Midtvedt, Benjamin, Pineda, Jesús, Sabirsh, Alan, Adiels, Caroline B., Romeo, Stefano, Midtvedt, Daniel, Volpe, Giovanni

Quantitative analysis of cell structures is essential for biomedical and pharmaceutical research. The standard imaging approach relies on fluorescence microscopy, where cell structures of interest are labeled by chemical staining techniques. However,

Externí odkaz: http://arxiv.org/abs/2012.12986

Quantitative Digital Microscopy with Deep Learning

Autor: Midtvedt, Benjamin, Helgadottir, Saga, Argun, Aykut, Pineda, Jesús, Midtvedt, Daniel, Volpe, Giovanni

Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithm

Externí odkaz: http://arxiv.org/abs/2010.08260

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