
The Practical Big Data Workshop held in the Michigan League Building from June 6-8, 2019
Opening Day Presentations
- Lei Xing: Medical Imaging and Treatment Planning in the Era of AI
- Lawrence Tarbox: Curating Data for Inclusion in The Cancer Imaging Archive (TCIA)
- Rich Caruana: Friends Don't Let Friends Deploy Black Box Models
- Jonathan Bona: Semantic Integration of Non-Image Data
- Chris Treml: The Three S's of AI: Standards, Standardization, and Scalability
- Keyvan Farahani: NCI Initiatives in Support of Big Data Research
Big Data in Imaging Apps
- John Christodouleas, MD, MPH: Accelerating MR-guided Biologic Adaptation with MOMENTUM
- Jeff Siewerdsen, PhD: Spine Cloud: Image Analytics and Predictive Models for Spine Surgery Outcomes
- Ke Sheng, PhD, FAAPM, DABR: Adaptive Radiotherapy Using Machine Learning
- Yoganand Balagurunathan, PhD: Habitats in Prostate Cancer- Finding the Aggressive Disease
- Tahsin Kurc: Studying Cancer Morphology with Gigapixel Images
- Aimilia Gastounioti, PhD: Breast Cancer Imaging Radiomics
- Lubomir Hadjiiski, PhD: QA Validation Of Definitions In Radiomics Feature Ontology
Big Data and AI-Enabling Standardization
- Mark Phillips, PhD: Key Data Elements, Relationship and Ontology
- Reid F Thompson, MD, PhD: Imaging Radiomics & Ontology
- Amanda Caissie, MD, PhD, FRCPC: Promoting Standardized Radiotherapy (RT) Nomenclature and Collection of Patient Reported Outcomes (PRO)- A Pan-Canadian Approach
- Charles Mayo, PhD: Creating Scalable Data Centric, Clinical Processes for Quality Datasets
- Neil Martin, MD: Operationalizing Large Scale Aggregation of Patient Reported Outcomes
- Alberto Traverso, PhD: Bridging the Gap: The need for clinical "-omics" data integration and standardization for rapid translation of research in the clinic
- Joseph Killoran, PhD and Neil Martin, MD, MPH: Big Data Comes From Small Data: Making Operational Data Visible
- Jeff Newell: Big Data and Machine Learning at Varian
AI and Machine-Learning Methodologies
- Rich Caruana: Intelligibility, Causality & Treatment Effects
- Yi Luo, PhD: Bayesian Networks and Causal Inference
- Mike Dusenberry: Bayesian Deep Learning for Medicine
- John Kang, MD, PhD: Data Processing: Cross Validation, Bias Control, Missing Data, Limited Size, and Data Heterogeneity
- Issam El Naqa, PhD: Safe Implementation & Quality Assurance Considerations For AI
- Olivier Morin, PhD: Part 1- MEDomics: A Framework for the Development of AI in Radiation Oncology
- Martin Vallières, PhD: Part 2- MEDomicsLab: An Open-source Computation Platform for Multi-omics Modeling in Medicine
Closing Day Presentations
- Randi Kudner: Perspectives on the Evolving Standardization Landscape
- Carlotta Masciocchi, PhD: The Distributed Ecosystem: A Solution for Developing Privacy- Preserving Predictive Models
- Gareth Price: The Practical Implementation of a UK Big Data Research Platform
- Matthew Field: Development of a Distributed Machine Learning Software Platform for Big Data Research
- Andre Dekker: Distributed Learning At Scale
- Reid Thompson, MD, PhD: Breakout Session 1 Summary- Big Data and Artificial Intelligence Enabling Standardizations
- John Kang, MD, PhD and Issam El Naqa, PhD: Breakout Session 2 Summary- Artificial Intelligence and Machine Learning Methodologies
- Kevyan Farahani, PhD and Ying Xiao, PhD: Breakout Session 3 Summary- Big Data in Imaging Applications
- Joe Deasy, PhD: Big Data Should Be In Search of Big Questions