Classifying Malignancy in Prostate Glandular Structures from Biopsy Scans with Deep Learning.

ABSTRACT: Histopathological classification in prostate cancer remains a challenge with high dependence on the expert practitioner. We develop a deep learning (DL) model to identify the most prominent Gleason pattern in a highly curated data cohort and validate it on an independent dataset. The histology images are partitioned in tiles (14,509) and are curated by an expert to identify individual glandular structures with assigned primary Gleason pattern grades. We use transfer learning and fine-tuning approaches to compare several deep neural network architectures that are trained on a corpus of camera images (ImageNet) and tuned with histology examples to be context appropriate for histopathological discrimination with small samples. In our study, the best DL network is able to discriminate cancer g

SUBMITTER: Fogarty R 

PROVIDER: S-EPMC10136774 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature