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Inferring biological relationships from cellular phenotypes in high-content microscopy screens provides significant opportunity and challenge in biological research.
Big Transfer (BiT): General Visual Representation Learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 1912
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CellProfiler: image analysis software for identifying and quantifying cell phenotypes
Anne E Carpenter, Thouis R Jones, Michael R Lamprecht, Colin Clarke, In Han Kang, Ola Friman, David A Guertin, Joo Han Chang, Robert A Lindquist, Jason Moffat, Polina Golland, and David M Sabatini · 2006
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CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
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Microscopy-Based High-Content Screening
Michael Boutros, Florian Heigwer, and Christina Laufer · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Classifying and segmenting microscopy images with deep multiple instance learning
Oren Z. Kraus, Jimmy Lei Ba, and Brendan J. Frey · 2016
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Automating Morphological Profiling with Generic Deep Convolutional Networks
Nick Pawlowski, Juan C Caicedo, Shantanu Singh, Anne E Carpenter, and Amos Storkey · 2016
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Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments
David A. Van Valen, Takamasa Kudo, Keara M. Lane, Derek N. Macklin, Nicolas T. Quach, Mialy M. DeFelice, Inbal Maayan, Yu Tanouchi, Euan A. Ashley, and Markus W. Covert · 2016
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Improving Phenotypic Measurements in High-Content Imaging Screens
D. Michael Ando, Cory Y. McLean, and Marc Berndl · 2017
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Data-analysis strategies for image-based cell profiling
Juan C Caicedo, Sam Cooper, Florian Heigwer, Scott Warchal, Peng Qiu, Csaba Molnar, Aliaksei S Vasilevich, Joseph D Barry, Harmanjit Singh Bansal, Oren Kraus, Mathias Wawer, Lassi Paavolainen, Markus D Herrmann, Mohammad Rohban, Jane Hung, Holger Hennig, John Concannon, Ian Smith, Paul A Clemons, Shantanu Singh, Paul Rees, Peter Horvath, Roger G Linington, and Anne E Carpenter · 2017
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Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes
Kevin Drew, Chanjae Lee, Ryan L Huizar, Fan Tu, Blake Borgeson, Claire D McWhite, Yun Ma, John B Wallingford, and Edward M Marcotte · 2017
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Philipp Eulenberg, Niklas Köhler, Thomas Blasi, Andrew Filby, Anne E. Carpenter, Paul Rees, Fabian J. Theis, and F. Alexander Wolf · 2017
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Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
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Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Elad Hoffer, Itay Hubara, and Daniel Soudry · 2017
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Automated analysis of high-content microscopy data with deep learning
Oren Z Kraus, Ben T Grys, Jimmy Ba, Yolanda Chong, Brendan J Frey, Charles Boone, and Brenda J Andrews · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Adaptive batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Xiaodi Hou, and Jiaying Liu · 2018
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A brief introduction to weakly supervised learning
Zhi-Hua Zhou · 2018
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CORUM: the comprehensive resource of mammalian protein complexes—2019
Madalina Giurgiu, Julian Reinhard, Barbara Brauner, Irmtraud Dunger-Kaltenbach, Gisela Fobo, Goar Frishman, Corinna Montrone, and Andreas Ruepp · 2019
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Deep learning for cellular image analysis
Erick Moen, Dylan Bannon, Takamasa Kudo, William Graf, Markus Covert, and David Van Valen · 2019
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Analysis of the Human Protein Atlas Image Classification competition
Wei Ouyang, Casper F. Winsnes, Martin Hjelmare, Anthony J. Cesnik, Lovisa Åkesson, Hao Xu, Devin P. Sullivan, Shubin Dai, Jun Lan, Park Jinmo, Shaikat M. Galib, Christof Henkel, Kevin Hwang, Dmytro Poplavskiy, Bojan Tunguz, Russel D. Wolfinger, Yinzheng Gu, Chuanpeng Li, Jinbin Xie, Dmitry Buslov, Sergei Fironov, Alexander Kiselev, Dmytro Panchenko, Xuan Cao, Runmin Wei, Yuanhao Wu, Xun Zhu, Kuan-Lun Tseng, Zhifeng Gao, Cheng Ju, Xiaohan Yi, Hongdong Zheng, Constantin Kappel, and Emma Lundberg · 2019
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A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets
A new era in functional genomics screens
Laralynne Przybyla and Luke A. Gilbert · 2022
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Deemd: Drug efficacy estimation against sars-cov-2 based on cell morphology with deep multiple instance learning
M Sadegh Saberian, Kathleen P Moriarty, Andrea D Olmstead, Christian Hallgrimson, François Jean, Ivan R Nabi, Maxwell W Libbrecht, and Ghassan Hamarneh · 2022
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Masked frequency modeling for self-supervised visual pre-training
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Damian Szklarczyk, Annika L Gable, Katerina C Nastou, David Lyon, Rebecca Kirsch, Sampo Pyysalo, Nadezhda T Doncheva, Marc Legeay, Tao Fang, Peer Bork, Lars J Jensen, and Christian von Mering · 2020
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Emerging Properties in Self-Supervised Vision Transformers
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David R. Stirling, Madison J. Swain-Bowden, Alice M. Lucas, Anne E. Carpenter, Beth A. Cimini, and Allen Goodman · 2021
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Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
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A Cookbook of Self-Supervised Learning
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Gpt-4 technical report, 2023
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