📣 Help Shape the Future of UKRI's Gateway to Research (GtR)

We're improving UKRI's Gateway to Research and are seeking your input! If you would be interested in being interviewed about the improvements we're making and to have your say about how we can make GtR more user-friendly, impactful, and effective for the Research and Innovation community, please email gateway@ukri.org.

Towards computationally efficient prediction of molecular signatures from routine histology images (2021)

First Author: Lafarge, Maxime W

Attributed to: MICA: Developing Micro-Community Analytics for Histology Landscapes (MiCAHiL) funded by MRC

Abstract

No abstract provided

Bibliographic Information

Digital Object Identifier: http://dx.doi.org/10.5167/uzh-208553

Publication URI: https://www.zora.uzh.ch/id/eprint/208553

Type: Other