作者
Marko van Treeck, Didem Cifci, Narmin Ghaffari Laleh, Oliver Lester Saldanha, Chiara ML Loeffler, Katherine J Hewitt, Hannah Sophie Muti, Amelie Echle, Tobias Seibel, Tobias Paul Seraphin, Christian Trautwein, Sebastian Foersch, Tom Luedde, Daniel Truhn, Jakob Nikolas Kather
发表日期
2021/12/19
期刊
BioRxiv
页码范围
2021.12. 19.473344
出版商
Cold Spring Harbor Laboratory
简介
The interpretation of digitized histopathology images has been transformed thanks to artificial intelligence (AI). End-to-end AI algorithms can infer high-level features directly from raw image data, extending the capabilities of human experts. In particular, AI can predict tumor subtypes, genetic mutations and gene expression directly from hematoxylin and eosin (H&E) stained pathology slides. However, existing end-to-end AI workflows are poorly standardized and not easily adaptable to new tasks. Here, we introduce DeepMed, a Python library for predicting any high-level attribute directly from histopathological whole slide images alone, or from images coupled with additional meta-data (https://github.com/KatherLab/deepmed). Unlike earlier computational pipelines, DeepMed is highly developer-friendly: its structure is modular and separates preprocessing, training, deployment, statistics, and visualization in such a way that any one of these processes can be altered without affecting the others. Also, DeepMed scales easily from local use on laptop computers to multi-GPU clusters in cloud computing services and therefore can be used for teaching, prototyping and for large-scale applications. Finally, DeepMed is user-friendly and allows researchers to easily test multiple hypotheses in a single dataset (via cross-validation) or in multiple datasets (via external validation). Here, we demonstrate and document DeepMed’s abilities to predict molecular alterations, histopathological subtypes and molecular features from routine histopathology images, using a large benchmark dataset which we release publicly. In summary, DeepMed is a fully integrated …
学术搜索中的文章
M van Treeck, D Cifci, NG Laleh, OL Saldanha… - BioRxiv, 2021