Solving Large-scale Medical Imaging AI Problems: Enabling Covid-19 Research at University of Wisconsin School of Medicine
Recorded on Tuesday, March 30th at 1 pm EDT
Recorded on Tuesday, March 30th at 1 pm EDT
Data management is key to the development of AI and often accounts for 80% of researchers’ workload leaving minimal time for analysis. There are many challenges to getting high-quality data into the hands of researchers and AI developers quickly and efficiently. Organizations need access to a high volume of data that is curated to common standards, has access to powerful computing resources and meets privacy and compliance standards.
Drawing upon experience in the Life Sciences, Can Akgun, SVP of Business Development at Flywheel, discusses streamlining the ingestion and organization of petabytes of diverse data from internal and external sources, including real world data, and Flywheel's ability to scale processing and support machine learning workflows.
Travis Richardson, President and Chief Product Officer of Flywheel, discusses how a research-first data platform is the solution to scale clinical research, fuel innovation, and empower your researchers.
Chief Product Officer Travis Richardson discusses how Flywheel manages imaging and related non-imaging data.
Chief Product Officer Travis Richardson discusses how Flywheel can be used to share data and algorithms for multi-site research.
Chief Product Officer Travis Richardson introduces data management and curation tools an support for training machine learning in Flywheel.
Travis Richardson, President and Chief Product Officer, presents an overview of Flywheel as an open and extensible platform that support data reuse and reproducibility.
Gunnar Schaefer, Co-Founder and Chief Technology Officer, discusses the machine learning workflow in Flywheel, including labeling, search, training and testing.
Dr. Maggie Mahan talks about curating BIDS data sets in Flywheel, exporting BIDS data sets, and analyzing BIDS data sets with Flywheel Gears.