Markus D. Herrmann
Harvard University
28 Papers
7 Citations
Markus D. Herrmann is an academic researcher from Harvard University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 7, co-authored 21 publications. Previous affiliations of Markus D. Herrmann include University of Zurich & University of Ulm.
Chat about Author
Papers
Data-analysis strategies for image-based cell profiling
Juan C. Caicedo,Sam Cooper,Florian Heigwer,Scott Warchal,Peng Qiu,Csaba Molnar,Aliaksei Vasilevich,Joseph Barry,Harmanjit Singh Bansal,Oren Kraus,Mathias Wawer,Lassi Paavolainen,Markus D. Herrmann,Mohammad Hossein Rohban,Jane Hung,Jane Hung,Holger Hennig,John Concannon,Ian Smith,Paul A. Clemons,Shantanu Singh,Paul Rees,Paul Rees,Peter Horvath,Peter Horvath,Roger G. Linington,Anne E. Carpenter +26 more
TL;DR: The steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images are introduced and techniques that have proven useful in each stage of the data analysis process are recommended on the basis of the experience of 20 laboratories worldwide that are refining their image- based cell-profiling methodologies.
Multiplexed protein maps link subcellular organization to cellular states.
TL;DR: A high-throughput method that achieves the detection of more than 40 different proteins in biological samples across multiple spatial scales, and develops a data-driven computer vision approach that generates multiplexed protein maps (MPMs), which comprehensively quantify intracellular protein composition with high spatial detail in large numbers of single cells.
468
NCI Imaging Data Commons.
Andrey Fedorov,William J.R. Longabaugh,David Pot,David A. Clunie,Steve Pieper,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts,André Homeyer,Robert F. Lewis,Afshin Akbarzadeh,Dennis Bontempi,William Clifford,Markus D. Herrmann,Henning Höfener,Igor Octaviano,Chad Osborne,Suzanne M. Paquette,James Petts,Davide Punzo,Madelyn Reyes,Daniela P. Schacherer,Mi Tian,George White,Erik Ziegler,Ilya Shmulevich,Todd Pihl,Ulrike Wagner,Keyvan Farahani,Ron Kikinis +29 more
TL;DR: The Imaging Data Commons (IDC) as discussed by the authors is a new component of CRDC supported by the Cancer Moonshot, which aims to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of de-identified imaging data and to support integrated analyses with non-imaging data.
Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade
Manuel Álvaro Berbís,David S. McClintock,Andrey Bychkov,J. A. W. M. van der Laak,Liron Pantanowitz,Jochen K. Lennerz,Jerome Cheng,Brett Delahunt,Lars Egevad,Catarina Eloy,Alton B. Farris,Filippo Fraggetta,Raimundo García Del Moral,Douglas J. Hartman,Markus D. Herrmann,Eva Hollemans,Kenneth A. Iczkowski,Aly Karsan,Mark Kriegsmann,M. E. Salama,John H. Sinard,J. Mark Tuthill,Bethany Jill Williams,Cesar Casado-Sanchez,V. Sánchez-Turrión,Antonio Luna,José Aneiros-Fernández,Jeanne Shen +27 more
TL;DR: In this paper , a survey gathered current expert perspectives and expectations regarding the role of AI in pathology from those with first-hand computational pathology and AI experience, concluding that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030.
58
Computer vision for image-based transcriptomics.
TL;DR: The setup of the experimental pipeline for image-based transcriptomics is discussed, and the algorithms that were developed to extract, at high-throughput, robust multivariate feature sets of transcript molecule abundance, localization and patterning in tens of thousands of single cells across the transcriptome are described.
48