Erik A. Burlingame

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 computational challenges in analyzing multiplexed imaging data and outlines community-driven approaches to address them through workshops and hackathons.

The paper develops open-source tools for analyzing multiplex tissue imaging data and demonstrates their application on a human breast cancer dataset.

The paper introduces a method to separate domain-specific information using a cross-domain autoencoder and orthogonal gate block for improved domain translation.

This paper introduces a deep learning method called SHIFT that translates histological images into virtual immunofluorescence images, improving efficiency and reducing costs in cancer diagnostics.

RESTORE normalizes intensity variations in multiplexed imaging data by using negative control cells to remove background noise and improve sample integration.