Keywords: Prostate, Prostate, BPH, PCa
Motivation: Accurate interpretation of prostate MRI demands a high level of expertise and deep learning models for prostate cancer (PCa) detection often suffer from low specificity.
Goal(s): To explore the value of annotation of benign prostatic hyperplasia (BPH) to prostate cancer (PCa) detection.
Approach: We retrospectively collected 96 patients with PCa and 92 patients with BPH, all scanned with PI-RADS protocol. Two deep learning models were built: Model1 only detected PCa while Model2 simultaneously detected BPH and PCa.
Results: Model2 achieved superb performance with test AUC of 0.995, outperforming Model1 whose test AUC was 0.770.
Impact: Explicitly using the BPH label improved the performance of PCa detection significantly, implying multi-task deep learning models targeting multiple diseases are not only more in line with the needs of clinical applications, but can also bring about performance improvement.
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