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.
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 (Presented at SPIE Medical Imaging: February 13, 2017; Published: 1 March 2017); https://doi.org/10.1117/12.2253642.
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