Automated high-grade prostate cancer detection and ranking on whole slide images

Chao-Hui Huang; Daniel Racoceanu

doi:10.1117/12.2253642

1 March 2017 Automated high-grade prostate cancer detection and ranking on whole slide images

Chao-Hui Huang, Daniel Racoceanu

Proceedings Volume 10140, Medical Imaging 2017: Digital Pathology; 101400A (2017) https://doi.org/10.1117/12.2253642
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Chao-Hui Huang and Daniel Racoceanu "Automated high-grade prostate cancer detection and ranking on whole slide images", Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400A (1 March 2017); https://doi.org/10.1117/12.2253642

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