publication . Other literature type . External research report . 2020

The Aachen Protocol for Deep Learning Histopathology: A hands-on guide for data preprocessing

Muti, Hannah Sophie; Loeffler, Chiara; Echle, Amelie; Heij, Lara R; Buelow, Roman D; Krause, Jeremias; Broderius, Laura; Niehues, Jan; Liapi, Georgia; Boor, Peter; ...
Open Access
  • Published: 03 Mar 2020
  • Publisher: Zenodo
<strong>Background</strong>: Deep learning can predict clinically relevant features such as genetic alterations directly from H&amp;E stained histology images. In practice, many clinically relevant questions are limited by availability of clinical data and by the lack of standardized preprocessing pipelines. In our research projects, we strive to keep a consistent data format across projects to facilitate downstream analysis. <strong>Workflow</strong>: We analyze cohorts of cancer patients and try to predict clinically relevant labels directly from whole slide images (WSI). To achieve this, we manually or automatically detect tumor tissue in the WSI, tessellate ...
Medical Subject Headings: education
free text keywords: deep learning, histopathology, cancer, data preprocessing
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Other literature type . 2020
Provider: Datacite
External research report . 2020
Provider: ZENODO
Other literature type . 2020
Provider: Datacite
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