Harnessing clinical annotations to improve deep learning performance in prostate segmentation.

Karthik V SarmaAlex G RamanNikhil J DhinagarAlan M PriesterStephanie HarmonThomas SanfordSherif MehralivandBaris TurkbeyLeonard S MarksSteven S RamanWilliam SpeierCorey W Arnold

Published in: PloS one (2021)

We trained a state-of-the-art model using unrefined clinical prostate annotations and found that its use as a template model significantly improved performance in other prostate segmentation tasks, even when trained with only 5% of the original dataset.

Keyphrases

deep learning
prostate cancer
benign prostatic hyperplasia
convolutional neural network
artificial intelligence
working memory
high resolution
molecularly imprinted
solid phase extraction