Practical Big Data Workshop

Directory of archived presentations from the 2019 workshop

Michigan League building at night
The Practical Big Data Workshop held in the Michigan League Building from June 6-8, 2019

Opening Day Presentations

  1. Lei Xing: Medical Imaging and Treatment Planning in the Era of AI
  2. Lawrence Tarbox: Curating Data for Inclusion in The Cancer Imaging Archive (TCIA)
  3. Rich Caruana: Friends Don't Let Friends Deploy Black Box Models
  4. Jonathan Bona: Semantic Integration of Non-Image Data
  5. Chris Treml: The Three S's of AI: Standards, Standardization, and Scalability
  6. Keyvan Farahani: NCI Initiatives in Support of Big Data Research

Big Data in Imaging Apps

  1. John Christodouleas, MD, MPH: Accelerating MR-guided Biologic Adaptation with MOMENTUM
  2. Jeff Siewerdsen, PhD: Spine Cloud: Image Analytics and Predictive Models for Spine Surgery Outcomes
  3. Ke Sheng, PhD, FAAPM, DABR: Adaptive Radiotherapy Using Machine Learning
  4. Yoganand Balagurunathan, PhD: Habitats in Prostate Cancer- Finding the Aggressive Disease
  5. Tahsin Kurc: Studying Cancer Morphology with Gigapixel Images
  6. Aimilia Gastounioti, PhD: Breast Cancer Imaging Radiomics
  7. Lubomir Hadjiiski, PhD: QA Validation Of Definitions In Radiomics Feature Ontology

Big Data and AI-Enabling Standardization

  1. Mark Phillips, PhD: Key Data Elements, Relationship and Ontology
  2. Reid F Thompson, MD, PhD: Imaging Radiomics & Ontology
  3. Amanda Caissie, MD, PhD, FRCPC: Promoting Standardized Radiotherapy (RT) Nomenclature and Collection of Patient Reported Outcomes (PRO)- A Pan-Canadian Approach
  4. Charles Mayo, PhD: Creating Scalable Data Centric, Clinical Processes for Quality Datasets
  5. Neil Martin, MD: Operationalizing Large Scale Aggregation of Patient Reported Outcomes
  6. Alberto Traverso, PhD: Bridging the Gap: The need for clinical "-omics" data integration and standardization for rapid translation of research in the clinic
  7. Joseph Killoran, PhD and Neil Martin, MD, MPH: Big Data Comes From Small Data: Making Operational Data Visible
  8. Jeff Newell: Big Data and Machine Learning at Varian

AI and Machine-Learning Methodologies

  1. Rich Caruana: Intelligibility, Causality & Treatment Effects
  2. Yi Luo, PhD: Bayesian Networks and Causal Inference
  3. Mike Dusenberry: Bayesian Deep Learning for Medicine
  4. John Kang, MD, PhD: Data Processing: Cross Validation, Bias Control, Missing Data, Limited Size, and Data Heterogeneity
  5. Issam El Naqa, PhD: Safe Implementation & Quality Assurance Considerations For AI
  6. Olivier Morin, PhD: Part 1- MEDomics: A Framework for the Development of AI in Radiation Oncology
  7. Martin Vallières, PhD: Part 2- MEDomicsLab: An Open-source Computation Platform for Multi-omics Modeling in Medicine

Closing Day Presentations

  1. Randi Kudner: Perspectives on the Evolving Standardization Landscape
  2. Carlotta Masciocchi, PhD: The Distributed Ecosystem: A Solution for Developing Privacy- Preserving Predictive Models
  3. Gareth Price: The Practical Implementation of a UK Big Data Research Platform
  4. Matthew Field: Development of a Distributed Machine Learning Software Platform for Big Data Research
  5. Andre Dekker: Distributed Learning At Scale
  6. Reid Thompson, MD, PhD: Breakout Session 1 Summary- Big Data and Artificial Intelligence Enabling Standardizations
  7. John Kang, MD, PhD and Issam El Naqa, PhD: Breakout Session 2 Summary- Artificial Intelligence and Machine Learning Methodologies
  8. Kevyan Farahani, PhD and Ying Xiao, PhD: Breakout Session 3 Summary- Big Data in Imaging Applications
  9. Joe Deasy, PhD: Big Data Should Be In Search of Big Questions