Gleason grading of prostate cancer using artificial intelligence: lessons learned from the PANDA challenge
Keywords: Computational pathology, prostate cancer, Gleason grading, challenge
TL;DR: Summary of the findings from the Prostate cancer grade assessment (PANDA) challenge
Abstract: Assessing prostate biopsies is crucial for the clinical management of patients with suspected prostate cancer, but is associated with complications such as inter-observer variability. The PANDA challenge aimed at mitigating these issues through development and rigorous validation of image analysis algorithms for the task. In this short paper, we summarize the key insights gained from PANDA from the viewpoints of algorithm development and challenge organisation.
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Paper Type: recently published or submitted journal contributions
Primary Subject Area: Application: Histopathology
Secondary Subject Area: Detection and Diagnosis
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