Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images.

ABSTRACT: The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference l

SUBMITTER: Steinbuss G 

PROVIDER: S-EPMC8156071 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature