Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides.

ABSTRACT: Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated inter- and intra-observer variability. Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78,047 hemosider

SUBMITTER: Marzahl C 

PROVIDER: S-EPMC7398908 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature