Semi-Supervised, Attention-Based Deep Learning for Predicting TMPRSS2:ERG Fusion Status in Prostate Cancer Using Whole Slide Images.
Ontology highlight
ABSTRACT: Implications
Our study illuminates the potential of deep learning in effectively inferring key prostate cancer genetic alterations from the tissue morphology depicted in routinely available histology slides, offering a cost-effective method that could revolutionize diagnostic strategies in oncology.
SUBMITTER: Omar M
PROVIDER: S-EPMC10985477 | biostudies-literature | 2024 Apr
REPOSITORIES: biostudies-literature
Publications
Semi-Supervised, Attention-Based Deep Learning for Predicting TMPRSS2:ERG Fusion Status in Prostate Cancer Using Whole Slide Images.
Omar Mohamed M Xu Zhuoran Z Rand Sophie B SB Alexanderani Mohammad K MK Salles Daniela C DC Valencia Itzel I Schaeffer Edward M EM Robinson Brian D BD Lotan Tamara L TL Loda Massimo M Marchionni Luigi L
Molecular cancer research : MCR 20240401 4
<h4>Implications</h4>Our study illuminates the potential of deep learning in effectively inferring key prostate cancer genetic alterations from the tissue morphology depicted in routinely available histology slides, offering a cost-effective method that could revolutionize diagnostic strategies in oncology. ...[more]
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