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Digital webinar master deck final

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Federated Learning (FL) is a learning paradigm that enables collaborative learning without centralizing datasets. In this webinar, NVIDIA present the concept of FL and discuss how it can help overcome some of the barriers seen in the development of AI-based solutions for pharma, genomics and healthcare. Following the presentation, the panel debate on other elements that could drive the adoption of digital approaches more widely and help answer currently intractable science and business questions.

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Digital webinar master deck final

  1. 1. Pistoia Alliance Webinar How Can Federated AI/ML Learning Support Genomics and Patient Data Analysis to Enable Precision Medicine at Scale? 4th May 2020 15.00 to 16.00 BST
  2. 2. ©PistoiaAlliance Agenda 2 Time Title Presenter 15:00 Introduction, housekeeping Zahid Tharia, Pistoia Alliance 15:05 Pistoia Alliance Digital Strategy Paul Denny-Gouldson, Pistoia Alliance 15:10 How can federated learning support genomics and enable precision medicine at scale? Craig Rhodes, EMEA Industry Lead for Healthcare and Life Science, NVIDIA Nicola Rieke, Senior Deep Learning Solution Architect – Healthcare, NVIDIA 15:35 Panel: How do we collect and leverage patient data to develop tools that support digital health, what data is important and what barriers exist? To include: Jennifer Goldsack – CEO Digital Medicine Society (DiMe) Tim McCarthy, Head of Digital Medicine & Translational Imaging, Pfizer Marissa Dockendorf, Director, Global Digital Analytics & Technologies, Merck 15:55 Wrap up Paul Denny-Gouldson, Pistoia Alliance
  3. 3. Introduction Paul Denny-Gouldson, Consultant Pistoia Alliance
  4. 4. Poll: With COVID-19 will the potential accelerated move to Digital be sustainable? Yes/No 4
  5. 5. ©PistoiaAlliance The Digital Seed project: Quality generation and ethical use of digital health data in clinical studies ❑ Collaboration with DiMe ❑ Project Funded by Pistoia - $40k ❑ Project Manager being recruited, Steering committee being formed ❑ Project Goals Make recommendations on developing EULAs and TOUs for digital technologies used in health and biomedical research. Accelerate digitally-powered investigator-initiated and sponsored research for the betterment of public health. ❑ Proposed Deliverables • Identify and synthesize existing best practices from bioethics, health, technology, data science, and cyber-security disciplines with regards to protecting confidentiality, privacy, and control over data • Articulate a standard lexicon of digital study types & define quality metrics for categorizing evidence used to designate digital tools as fit-for- purpose in a clinical application - limit to clinical trials to constrain scope • Develop consensus recommendations and resources for the development and use of digital tools for data capture • Disseminate recommendations and resources to the manufacturers of digital tools, patient communities conducting citizen science, regulators and policymakers, IRBs, research sponsors, and investigators ❑ We need more steering committee members – please volunteer if you are interested
  6. 6. ©PistoiaAlliance Other IP3 Digital Projects 6 2107 Building a strong evidentiary base for the adoption of digital medicine tools to support clinical applications 2105 Assembling domain-centric digital data sets for use as a testing environment for new digital health measures 2104 Driving multi-stakeholder acceptance of patient generated health data (PGHD) for use in clinical trials 2102 Advancing the Ethical Oversight of Biomedical Research to Keep Pace with Rapid Advancements in Digital Technology 2101 Powering studies using a sensor to generate data to inform the endpoints 2100 Quality generation and ethical use of digital health data in clinical studies If anyone is interested in any of these areas then please get in touch with us
  7. 7. How Can Federated Learning Support Genomics and Enable Precision Medicine at Scale? Craig Rhodes, EMEA Industry Lead for Healthcare and Life Science, NVIDIA Nicola Rieke, Senior Deep Learning Solution Architect – Healthcare, NVIDIA
  8. 8. Nicola Rieke | Sr. Deep Learning Solution Architect - Healthcare Craig Rhodes| EMEA IBD for AI/Deep Learning – Health and Life Science HOW CAN FEDERATED LEARNING SUPPORT GENOMICS AND ENABLE PRECISION MEDICINE AT SCALE?
  9. 9. 9 AI IN MEDICINE RADIOLOGY CT, MRI, US, X-RAY PATHOLOGY TISSUE & CELL DERMATOLOGY OPHTHALMOLOGY ELECTRONIC HEALTH RECORDS ... 27K Medical AI papers ~30 FDA Approved products ~7 Billion USD investment by 2021 GENOMICS
  10. 10. 10 FROM RESEARCH... Improving state of the art performance in controlled settings
  11. 11. 11 ...TO APPLICATIONS Achieving human-level performance on large data and clinical settings Hegde et al., Similar Image Search for Histopathology: SMILY, Nature digital medicine 2019 • 127000 image patches • 128,000,000 8 × 8 μm regions • Histopathology image search Luo et al., Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study, Lancet Oncology 2019 • 6 hospitals in China • 84424 individuals • 1036496 endoscopy images • Gastrointestinal cancer detection • Perf. similar to the expert endoscopist Esteva et al, Dermatologist-level classification of skin cancer with deep neural networks, Nature 2017 • 129450 clinical images • 2032 diseases • Skin cancer detection • comparable to dermatologists De Fauw et al., Clinically applicable deep learning for diagnosis and referral in retinal disease, Nature Medicine 2018 • “Only” 14884 OCT 3D scans • Resolution ~ 5 µm • Volumetric multi-region segmentation • Performance comparable to humans
  12. 12. 12 DATA-DRIVEN MEDICINE REQUIRES FEDERATED EFFORTS - Training of robust and accurate DL models requires large and diverse datasets - Research is driven by data lakes - Health data is highly sensitive, subject to regulations and cannot easily be shared - Regulatory and legal challenges related to ethics, privacy and data protection, but also technical ones Data Governance and Privacy Demographic Bias / Healthcare in remote areas / Hindered Research (e.g. Rare Diseases) ? Data GPU Model Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M. & Cardoso, M. J. (2020). The Future of Digital Health with Federated Learning. preprint arXiv:2003.08119.
  13. 13. 13 Data GPU Model Data GPU Model Collaboration ? Possible Solution: Training algorithms collaboratively without sharing the raw data? DATA-DRIVEN MEDICINE REQUIRES FEDERATED EFFORTS Data Governance and Privacy Federated Learning – allow algorithms to learn from non co-located data
  14. 14. 14 DATA-DRIVEN MEDICINE REQUIRES FEDERATED EFFORTS - Address privacy and governance challenges - FL could create new opportunities - Large-scale validation directly in the institutions - Enable novel research (e.g. rare diseases) - Medical data is not duplicated - Privacy protection with differential privacy The Promise of Federated Efforts Federated Learning learning paradigm in which multiple parties train collaboratively without centralizing datasets Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M. & Cardoso, M. J. (2020). The Future of Digital Health with Federated Learning. preprint arXiv:2003.08119.
  15. 15. 15 IMPACT OF FEDERATED LEARNING Increasing the value of AI for all healthcare stakeholders Clinicians Accurate assistance tools, Standardization Patients Accurate and unbiased AI, Data donor Researchers Access to large datasets, Clinical relevant problems Hospital and Practices Full control of patient data, Infrastructure Manufacturers Continuous improvement of ML-based systems Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M. & Cardoso, M. J. (2020). The Future of Digital Health with Federated Learning. preprint arXiv:2003.08119.
  16. 16. 16 FEDERATED LEARNING • Broadly speaking, FL can be formalised as: Let denote a global loss function via a weighted combination of K local losses computed on private data • Rooted in older forms of collaborative learning. Main characteristics: • Datasets are distributed • Shared model Definition Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M. & Cardoso, M. J. (2020). The Future of Digital Health with Federated Learning. preprint arXiv:2003.08119.
  17. 17. 17 FEDERATED LEARNING Communication Architectures Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M. & Cardoso, M. J. (2020). The Future of Digital Health with Federated Learning. preprint arXiv:2003.08119.
  18. 18. 18 SERVER-CLIENT FEDERATED LEARNING Changing the way AI algorithms are trained GPU GPU GPU
  19. 19. 19 SERVER-CLIENT FEDERATED LEARNING Changing the way AI algorithms are trained GPU GPU GPU
  20. 20. 20 SERVER-CLIENT FEDERATED LEARNING Changing the way AI algorithms are trained GPU GPU GPU
  21. 21. 21 SERVER-CLIENT FEDERATED LEARNING Changing the way AI algorithms are trained GPU GPU GPU
  22. 22. 22 SERVER-CLIENT FEDERATED LEARNING Changing the way AI algorithms are trained GPU GPU GPU Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., ... & Feng, A. (2019, October). Privacy-preserving Federated Brain Tumour Segmentation. In International Workshop on Machine Learning in Medical Imaging (pp. 133-141). Springer, Cham.
  23. 23. 23 PERFORMANCE & ACCURACY EXPERIMENTS • Multi-modal multi-class brain tumour seg. • 242 subjects • Data-centralised training • Federated averaging • 13 clients Privacy-preserving Federated Brain Tumour Segmentation. arxiv.org/abs/1910.00962 Federated Learning building Robust AI
  24. 24. 24 TECHNICAL CONSIDERATIONS FL does not solve all potential privacy issues. Privacy – preserving techniques for FL offer levels of protection that exceed general ML models. Depending on level of trust in FL consortium, different counter-measures may be implemented. PRIVACY & SECURITY Medical data is particularly diverse (type, dimensionality, …). This poses a challenge if data is not independent and identically distributed across participants. Global solution may not be the optimal local solution. DATA HETEROGENEITY In particular in non-trusted federations, traceability and accountability processes require execution integrity. It may also be helpful to measure the amount of contribution from each participant to determine relevant compensation and establish a revenue model among the participants. TRACEABILITY & ACCOUNTABILITY SYSTEM ARCHITECTURE Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M. & Cardoso, M. J. (2020). The Future of Digital Health with Federated Learning. preprint arXiv:2003.08119. Local training in each institution requires computational infrastructure available on-site. Data and annotation needs to be standardized. For enabling the collaborative training, a training protocol is needed.
  25. 25. 25 HEALTHCARE INDUSTRY ADOPTING FEDERATED LEARNING MEDICAL IMAGING Adopting NVIDIA Clara Federated Learning for Imaging PHARMA Machine Learning Ledger Orchestration for Drug Discovery PHARMA PARTNERS PUBLIC PARTNERS This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement N°831472. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA
  26. 26. 26 FEDERATED LEARNING EXPERIMENT
  27. 27. 27 FEDERATED LEARNING EXPERIMENT - Breast density is a significant risk factor for breast cancer. - Women with a high mammographic breast density (>75%) have a four- to six-fold increased breast cancer risk compared with women having a very low breast density. - High mammographic breast density impairs the sensitivity and specificity of breast cancer screening, possibly because present (small) malignant lesions are not detectable. Classification of Breast Imaging Reporting and Data System (BI-RADS) Breast Density in 2D Mammography Ooms, E., et al. (2007). "Mammography: interobserver variability in breast density assessment." The Breast 16(6): 568-576.
  28. 28. 28 FEDERATED LEARNING EXPERIMENT Classification of Breast Imaging Reporting and Data System (BI-RADS) Breast Density in 2D Mammography Lehman, C. D., et al. (2019). "Mammographic breast density assessment using deep learning: clinical implementation." Radiology 290(1): 52-58.
  29. 29. 29 FEDERATED LEARNING EXPERIMENT Classification of Breast Imaging Reporting and Data System (BI-RADS) Breast Density in 2D Mammography
  30. 30. 30 FEDERATED LEARNING EXPERIMENT Classification of Breast Imaging Reporting and Data System (BI-RADS) Breast Density in 2D Mammography
  31. 31. 31 FEDERATED LEARNING EXPERIMENT Average model performance
  32. 32. 32 Distributed Collaborative Learning Build a common, robust AI model without sharing data Using NVIDIA Clara to: Authenticate and deliver Clara FL to participating hospitals Locally train on private data Securely Share partial-model weights Apply Federated Averaging creating a new global model BYOC to Federated Learning - New Addressing Data Diversity & Privacy Global Model w CLARA FEDERATED LEARNING Collaborative Distributed Learning
  33. 33. 33 BRINGING STATE-OF-THE-ART AI TO HEALTHCARE
  34. 34. 34 NVIDIA IN HEALTHCARE BREAKTHROUGHS AI STARTUPS RESEARCHIMAGING DRUG DISCOVERY DEVICES
  35. 35. 35 FEDERATED LEARNING FUTURE OF AI
  36. 36. 36 FEDERATED LEARNING Clara Federated Learning: https://developer.nvidia.com/clara-medical-imaging Papers: • The Future of Digital Health with Federated Learning: https://arxiv.org/abs/2003.08119 • Privacy-preserving Federated Brain Tumour Segmentation: https://arxiv.org/abs/1910.00962 • Federated Deep Learning Among Multiple Institutions for Automated Classification of Breast Density: https://cdn.ymaws.com/siim.org/resource/resmgr/siim20/abstracts-research/chang-kalpathycramer_federat.pdf Blog: • https://blogs.nvidia.com/blog/2019/10/13/what-is-federated-learning/ • https://devblogs.nvidia.com/federated-learning-clara/ Resources
  37. 37. Poll: What Data is most important to support digital pharma and health? 1. Lab Test results 2. Omics / Biobank data 3. Lifestyle + food 4. Trials data + Medical history 5. Depends on area of focus
  38. 38. Panel: Jennifer Goldsack – CEO Digital Medicine Society (DiMe) Tim McCarthy, Head of Digital Medicine & Translational Imaging, Pfizer Marissa Dockendorf, Director, Global Digital Analytics & Technologies, Merck
  39. 39. ©PistoiaAlliance Question one 40 • How do we collect and leverage patient data to develop tools that support digital health, what data is important and what barriers exist?
  40. 40. ©PistoiaAlliance Question two 41 • Where do you see the next steps for “testing” in trials and what might a remote testing model in the near term look like?
  41. 41. ©PistoiaAlliance Question Three 42 • How can we work collaboratively once data is collected?
  42. 42. Panel Wrap up
  43. 43. Poll What do you see as the biggest barrier to collecting and leveraging patient data to support digital health using federated AI/ML learning? 1. Costs 2. Lack of skills & technology 3. Lack of industry-wide data standards 4. Industry regulation 5. Internal resistance
  44. 44. Get Involved! Digital, FAIR, AI/ML – IP3 project list Steering Committee Paul Denny-Gouldson paul.denny-Gouldson@pistoiaalliance.org Membership: Beeta Balali-Mood beeta.balalimood@pistoiaalliance.orgbeeta.balalimood@pistoiaalliance.o r General Enquiries: Zahid Tharia – zahid.tharia@pistoiaalliance.org www.pistoiaalliance.orgwww.pistoiaalliance.org
  45. 45. Next Webinar UXLS "Happy Hour": Heartificial Intelligence - A Human Centered Approach Wed, May 13, 2020, 16:00 – 17:00 BST
  46. 46. info@pistoiaalliance.org @pistoiaalliance www.pistoiaalliance.org

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