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Automated high-grade prostate cancer detection and ranking on whole slide images
Author(s): Chao-Hui Huang; Daniel Racoceanu
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Paper Abstract

Recently, digital pathology (DP) has been largely improved due to the development of computer vision and machine learning. Automated detection of high-grade prostate carcinoma (HG-PCa) is an impactful medical use-case showing the paradigm of collaboration between DP and computer science: given a field of view (FOV) from a whole slide image (WSI), the computer-aided system is able to determine the grade by classifying the FOV. Various approaches have been reported based on this approach. However, there are two reasons supporting us to conduct this work: first, there is still room for improvement in terms of detection accuracy of HG-PCa; second, a clinical practice is more complex than the operation of simple image classification. FOV ranking is also an essential step. E.g., in clinical practice, a pathologist usually evaluates a case based on a few FOVs from the given WSI. Then, makes decision based on the most severe FOV. This important ranking scenario is not yet being well discussed. In this work, we introduce an automated detection and ranking system for PCa based on Gleason pattern discrimination. Our experiments suggested that the proposed system is able to perform high-accuracy detection (~95:57% ± 2:1%) and excellent performance of ranking. Hence, the proposed system has a great potential to support the daily tasks in the medical routine of clinical pathology.

Paper Details

Date Published: 1 March 2017
PDF: 8 pages
Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400A (1 March 2017); doi: 10.1117/12.2253642
Show Author Affiliations
Chao-Hui Huang, MSD International GmbH (Singapore)
Agency for Science, Technology and Research (Singapore)
Daniel Racoceanu, Pontifical Catholic Univ. of Peru (France)
Sorbonne Univ., Univ Paris 06, CNRS (France)


Published in SPIE Proceedings Vol. 10140:
Medical Imaging 2017: Digital Pathology
Metin N. Gurcan; John E. Tomaszewski, Editor(s)

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