Decidual Vasculopathy Identification in Whole Slide Images Using Multi-Resolution Hierarchical Convolutional Neural Networks.

Decidual Vasculopathy Identification in Whole Slide Images Using Multi-Resolution Hierarchical Convolutional Neural Networks. Am J Pathol. 2020 Jul 14;: Authors: Clymer D, Kostadinov S, Catov J, Skvarca L, Pantanowitz L, Cagan J, LeDuc P Abstract After a child is born, the examination of the placenta by a pathologist for abnormalities such as infection or maternal vascular malperfusion can provide important information about the immediate and long-term health of the infant. Detection of the pathologic placental blood vessel lesion decidual vasculopathy (DV) has been shown to predict adverse pregnancy outcomes such as pre-eclampsia, which can lead to mother and neonatal morbidity in subsequent pregnancies. However, due to the high volume of deliveries at large hospitals and limited resources, currently a large proportion of delivered placentas are being discarded without inspection. Further, the correct diagnosis of DV often requires the expertise of an experienced perinatal pathologist. We introduce a hierarchical machine learning approach for the automated detection and classification of DV lesions in digitized placenta slides, along with a method of coupling learned image features with patient metadata in order to predict the presence of DV. Ultimately, the approach will allow many more placentas to be screened in a more standardized manner, providing feedback about which cases would benefit most from more in-depth pathological ins...
Source: The American Journal of Pathology - Category: Pathology Authors: Tags: Am J Pathol Source Type: research