Luke Ternes

The paper proposes deep learning methods to reconstruct 3D CyCIF images and select optimal ROIs from histology and MTI data for tumor representation.

The paper discusses challenges in digital image analysis of cancerous tissues and outlines solutions developed during a 2022 hackathon.

The paper develops computational methods to reduce multiplex imaging panels while retaining maximum information in breast cancer tissue microarrays.

The paper introduces ME-VAE, a multi-encoder VAE for single-cell image analysis that extracts transform-invariant biologically meaningful features, improving cell population separation and phenotypic differences.

The paper introduces a deep learning tool for automated histologic feature quantification in murine pancreatic disease models, showing high correlation with immunostaining results and accurate differentiation of disease states.