Computational pathology: Challenges and promises for tissue analysis
- Creators
- Fuchs, Thomas J.
- Buhmann, Joachim M.
Abstract
The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.
Additional Information
© 2011 Elsevier Ltd. Received 29 June 2010; revised 21 January 2011; Accepted 23 February 2011. Available online 9 April 2011. The authors wish to thank Holger Moch and Peter Schraml for their help in conducting the RCC TMA project, Peter Wild and Peter Bode for annotating the medical data and Monika Bieri and Norbert Wey for scanning and tiling the TMA slides. Special thanks also to Volker Roth and Sudhir Raman for valuable discussions. We also acknowledge financial support from the FET program within the EU FP7, under the SIMBAD project (Contract 213250).Additional details
- Eprint ID
- 27541
- DOI
- 10.1016/j.compmedimag.2011.02.006
- Resolver ID
- CaltechAUTHORS:20111101-083725218
- 213250
- European Union FP7 SIMBAD Project
- Created
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2011-11-01Created from EPrint's datestamp field
- Updated
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2021-11-09Created from EPrint's last_modified field