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Deep Learning for Prostate Pathology

by   Okyaz Eminaga, et al.

The current study detects different morphologies related to prostate pathology using deep learning models; these models were evaluated on 2,121 hematoxylin and eosin (H E) stain histology images that spanned a variety of image qualities, origins (Whole-slide, Tissue micro array, Whole mount, Internet), scanning machines, timestamps, H E staining protocols, and institutions. All histology images were captured using the bright field microscopy. For case use, these models were applied for the annotation tasks in clinician-oriented (cMDX) reports for prostatectomy specimens. The true positive rate (TPR) for slides with prostate cancer was 99.1 Gleason patterns reported in cMDX reports range between 0.795 and 1.0 at case level (n=55). TPR was 93.6 ductal morphology. The R-squared for the relative tumor volume was 0.987 between the ground truth and the prediction. Our models cover the major prostate pathology and successfully accomplish the annotation tasks.


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