GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images

• Type of Material: Article
• Authors: Pooya Mobadersany, Emory University; Lee Cooper, Emory University; Jeffery A Goldstein, Northwestern University, Chicago
• Language: English
• Date: 2021-03-05
• Publisher: Springer Nature [academic journals on nature.com]
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s), under exclusive licence to United States and Canadian Academy of Pathology 2021
• Final Published Version (URL): http://dx.doi.org/10.1038/s41374-021-00579-5
• Title of Journal or Parent Work: LABORATORY INVESTIGATION
• Volume: 101
• Issue: 7
• Start Page: 942
• End Page: 951
• Grant/Funding Information: JAG was supported by funding from the National Institutes of Health National Institute of Biomedical Imaging and Bioengineering (NIBIB K08EB030120). Slide scanning was supported by the Northwestern University Pathology Core Facility and a Cancer Center Support Grant (NCI CA060553). PM and LADC were supported by funding from the National Institutes of Health National Cancer Institute Grants (NCI U24CA19436201 and NCI U01CA220401).
• Keywords: Software; Reproductive biology; Optical imaging
• Research Categories: Health Sciences, Pathology

Abstract

The placenta is the first organ to form and performs the functions of the lung, gut, kidney, and endocrine systems. Abnormalities in the placenta cause or reflect most abnormalities in gestation and can have life-long consequences for the mother and infant. Placental villi undergo a complex but reproducible sequence of maturation across the third-trimester. Abnormalities of villous maturation are a feature of gestational diabetes and preeclampsia, among others, but there is significant interobserver variability in their diagnosis. Machine learning has emerged as a powerful tool for research in pathology. To capture the volume of data and manage heterogeneity within the placenta, we developed GestaltNet, which emulates human attention to high-yield areas and aggregation across regions. We used this network to estimate the gestational age (GA) of scanned placental slides and compared it to a baseline model lacking the attention and aggregation functions. In the test set, GestaltNet showed a higher r2 (0.9444 vs. 0.9220) than the baseline model. The mean absolute error (MAE) between the estimated and actual GA was also better in the GestaltNet (1.0847 weeks vs. 1.4505 weeks). On whole-slide images, we found the attention sub-network discriminates areas of terminal villi from other placental structures. Using this behavior, we estimated GA for 36 whole slides not previously seen by the model. In this task, similar to that faced by human pathologists, the model showed an r2 of 0.8859 with an MAE of 1.3671 weeks. We show that villous maturation is machine-recognizable. Machine-estimated GA could be useful when GA is unknown or to study abnormalities of villous maturation, including those in gestational diabetes or preeclampsia. GestaltNet points toward a future of genuinely whole-slide digital pathology by incorporating human-like behaviors of attention and aggregation.