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

The Practical Big Data Workshop series brings together global innovators in radiation therapy and diagnostic imaging who are leading in the development and use of big data and artificial intelligence to improve care for cancer patients. The workshop combines lectures, lively point-counterpoint debates, round table discussions, high-top  laptop presentations, and expansive socializing to build both community and science.

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