This Conference Presentation, “Reading room design in the COVID-19 era: what have we learned and what can we do better” was recorded for the Imaging Informatics for Healthcare, Research, and Applications conference at SPIE Medical Imaging 2022.

This Conference Presentation, “Clinical implementation of deep learning in the radiology workflow: opportunities and challenges” was recorded for the Imaging Informatics for Healthcare, Research, and Applications conference at SPIE Medical Imaging 2022.

This conference presentation "Opportunities for advanced imaging and novel technologies" was recorded for the Imaging Informatics for Healthcare, Research, and Applications conference at SPIE Medical Imaging 2022

This conference presentation "Optimizing quantitative imaging (QI) methods to create image biomarkers" was recorded for the Imaging Informatics for Healthcare, Research, and Applications conference at SPIE Medical Imaging 2022

This conference presentation, “Creating systems and infrastructure for sharing data and/or algorithms” was recorded for the Imaging Informatics for Healthcare, Research, and Applications conference at SPIE Medical Imaging 2022.

In this paper we demonstrate an automated pipeline for segmentation of the optic disk and hard exudates in retinal fundus images. The image is preprocessed to normalize luminosity and contrast throughout the image, and blood vessels are removed to reduce interference with the high-contrast properties of the optic disk and hard exudates. A pretrained VGG-16 U-net convolutional neural network is used for segmentation of the optic disk and ROI definition for hard exudates. The exudates are then segmented with a filtering method to isolate a rough segmentation, which is used to identify inputs to a U-net pretrained on exudate regions.

We create an informatics web application which uses algorithmic analysis of historical cases to introduce uniformity and data driven methods into the radiation therapy treatment process by providing treatment planning templates and treatment benchmarking. The database the system uses consists of historical DICOM RT objects from which we extract spatial quantitative features. These values are used to generate a list of historical cases from our database similar to a current case which a physician can then use as templates for treatment planning. Our system aims to introduce uniformity of methods and data driven methods into radiation therapy treatment planning.

This Poster Presentation, “Multi-view graph neural network for interpreting multi-omics biomarkers in Schizophrenia” was presented for the Imaging Informatics for Healthcare, Research, and Applications conference at SPIE Medical Imaging 2022.