Travis J. Hollmann

The paper introduces Nimbus, a deep learning model for predicting marker positivity in multiplexed imaging data using a pre-trained dataset, and shows its integration with clustering for cell subtype identification.

The paper introduces Mesmer, a deep learning algorithm for cell segmentation in tissue images, achieving human-level performance and enabling automated feature extraction.

The paper constructs a spatiotemporal atlas of the maternal-fetal interface using multiomic data to show how gestational age influences immune and stromal cell interactions and spiral artery remodeling.

The paper introduces DeepLIIF, a deep learning framework that translates IHC images to multiplex IF images and quantifies single-cell IHC markers.