Workshop Chairs: Olivier Colliot, Ctr. National de la Recherche Scientifique (France) and Ivana Išgum, Amsterdam UMC (Netherlands)

Data warehouses are increasingly built by hospitals and medical institutions. They gather data on a very large scale and contain of wide range of data types and acquisition protocols. For these reasons, data warehouses offer fantastic opportunities to build artificial intelligence (AI) tools which are efficient, robust and generalize well to realistic clinical scenarios. However, building such data warehouses and using them for AI medical imaging research also pose considerable challenges. This workshop will present different initiatives involving building and using data warehouses, discuss the associated challenges, the proposed solutions and the remaining open questions.

1:25 PM
Radiology data warehouses: data preparation and dissemination

Ronald M. Summers Senior Investigator National Institute of Health (United States)

The workflow for data preparation and dissemination is one of the key attributes of a successful large-scale radiology AI section. The workflow consists of identifying relevant images, downloading, anonymizing and converting them to more usable formats, identifying the imaging series of interest within a study, and coordinating clinical, text and imaging data. Successful dissemination of large-scale imaging data involves additional considerations that can prove challenging. In this presentation, I will provide an overview of my lab’s perspective on these topics and discuss case studies from both my institution and a collaborating institution

1:50 PM
Design and implementation of a real-time research data warehouse: lessons learned from Vanderbilt's ImageVU

Bennett A. Landman Professor and Department Chair Vanderbilt Univ. (United States)

The field of artificial intelligence in medical imaging is undergoing explosive growth, and Radiology is a prime target for innovation. Research labs and industry innovators are rapidly developing and presenting these methods (e.g., at venues such as SPIE Medical Imaging). Deploying AI tools within a clinical enterprise, even on limited retrospective evaluation, is complicated by security and privacy concerns. Thus, integrating innovations must be weighed against the substantive resources required for local clinical evaluation. To reduce barriers to validation while maintaining rigorous security and privacy standards, Vanderbilt has developed and maintained a research data warehouse for imaging since 2011. Most recently, we integrated an AI Imaging Incubator that serves as a storage destination within a clinical enterprise where images can be directed for novel research evaluation under Institutional Review Board approval. This presentation will discuss our design process and the evolving research pressures that shape our ecosystem.

2:15 PM
Exploiting hospital data warehouses: the challenges of image quality and heterogeneity

Ninon Burgos Research Scientist CNRS - Paris Brain Institute (France)

Most machine learning-based computer-aided diagnosis approaches have been designed and validated using research data sets. It is thus not clear how they would generalise to clinical routine and ultimately what is their medical value. We are currently exploiting real-life data from hospital data warehouses (specifically the data warehouse of the 39 Greater Paris hospitals - AP-HP, comprising millions of patients) to perform such evaluation. This talk will present the challenges we have to deal with related to the quality and heterogeneity of the imaging data. It will also highlight the solutions we have proposed.

2:40 PM
Panel Discussion on AI Using Large-Scale Data Warehouses
Enjoy a discussion on the topics with a panel assembled from the speakers, moderated by the chairs.

---

This special event is part of the Image Processing conference.