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Deep-Learning and HPC to Boost Biomedical applications for health

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The health crisis due to COVID-19 is shaping a new reality in which the exchange and access to health data in a secure way will be more and more necessary. In this complex challenge converge both the respect for the individual rights as well as the interests of the patients and the need to promote the research in pursuit of the public interest. To face this challenge, we can find different approaches across Europe. In this webinar, we will present the experiences of three EU-funded projects (BigMedilytics, BodyPass, and DeepHealth), besides an overview of the legal framework and recommendations to enforce both national regulations and GDPR by an expert in data privacy and security.

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Deep-Learning and HPC to Boost Biomedical applications for health

  1. 1. 1 The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111.The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111. BDV ppp virtual workshop: How to exchange and grant access to heath data in a secure way: approaches and recommendations Monica Caballero (Project Manager) - monica.caballero.galeote@everis.com Jon Ander Gómez (Technical Manager) - jon@upv.es May 14th 2020 Deep-Learning and HPC to Boost Biomedical Applications for Health
  2. 2. 4 The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111. About DeepHealth Aim & Goals • Put HPC computing power at the service of biomedical applications with DL needs and apply DL techniques on large and complex image biomedical datasets to support new and more efficient ways of diagnosis, monitoring and treatment of diseases. • Facilitate the daily work and increase the productivity of medical personnel and IT professionals in terms of image processing and the use and training of predictive models without the need of combining numerous tools. • Offer a unified framework adapted to exploit underlying heterogeneous HPC and Cloud architectures supporting state-of-the-art and next-generation Deep Learning (AI) and Computer Vision algorithms to enhance European-based medical software platforms. Duration: 36 months Starting date: Jan 2019 Budget 14.642.366 € EU funding 12.774.824 € 22 partners from 9 countries: Research centers, Health organizations, large industries and SMEs Research Organisations Health Organisations Large Industries SMEs Key facts
  3. 3. 5 The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111. Developments & Expected Results • The DeepHealth toolkit • Free and open-source software: 2 libraries + front-end. • EDDLL: The European Distributed Deep Learning Library • ECVL: the European Computer Vision Library • Ready to run algorithms on Hybrid HPC + Cloud architectures with heterogeneous hardware (Distributed versions of the training algorithms) • Ready to be integrated into end-user software platforms or applications • HPC infrastructure for an efficient execution of the training algorithms which are computational intensive by making use of heterogeneous hardware in a transparent way • Seven enhanced biomedical and AI software platforms provided by EVERIS, PHILIPS, THALES, UNITO, WINGS, CRS4 and CEA that integrate the DeepHealth libraries to improve their potential • Proposal for a structure for anonymised and pseudonymised data lakes • Validation in 14 use cases (Neurological diseases, Tumor detection and early cancer prediction, Digital pathology and automated image annotation). EU libraries
  4. 4. 6 The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111. The Concept SOFTWARE PLATFORM BIOMEDICAL APPLICATION Medical image (CT , MRI , X-Ray,…) Health professionals (Doctors & Medical staff) ICT Expert users New image to be analysed Prediction Score and/or highlighted RoI returned to the doctor Load trained predictive models to the platform Use toolkit to train predictive models Data used to train models Platform uses the toolkit to train models (optional) Images labelled by doctors Add validated images Production environment Training environment Using loaded/trained predictive models (Optional) Train predictive models using the platform and the toolkit Samples to be pre-processed and validated Images pending to be pre-processed and validated HPC infrastructure TOOLKIT
  5. 5. 7 The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111. Current status at May 2020 (M1–M16) • Design & specification of libraries APIs, HPC system and use-cases finished. • Definition of the structure for anonymised and pseudonymised data lakes in progress • DeepHealth Toolkit: • EDDL & ECVL: • First stable versions publicly available in GitHub (non-distributed): https://github.com/deephealthproject/eddl / | https://github.com/deephealthproject/ecvl • Work in progress: improving non-distributed version, Python wrapper, distributed version using COMPSs from BSC to run on hybrid HPC + Cloud architectures, also using FPGAs kernels • Front-end: • GUI mostly finished • Supporting back-end to load and transform images on the fly during training ready
  6. 6. 8 The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111. Current status at May 2020 (M1–M16) • HPC Infrastructure Adaptation – in progress: • Development of heterogeneous support for the DeepHealth libraries • Communication and Synchronisation optimizations to make training and inference algorithms efficient • HPC runtime support and custom hardware support for both libraries • Design an hybrid cloud computing solution to support DeepHealth libraries • Integration of libraries and use cases in application platforms— in progress: • Adaptation of application platforms to use cases specifics • Design of the “how to” integrate libraries into the platforms finished. Iterative integration in progress • Testing and pilots: • About to start 1st technical validation setting complete pipelines • Example: https://github.com/deephealthproject/use_case_pipeline • Use-cases preparing data-sets for pilots (acquiring, labelling, curation, etc.)
  7. 7. 9 The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111. Challenges/difficulties identified 1. Data sharing among project’s partners is not always possible 2. Data is not curated enough
  8. 8. 10 The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111. Challenge 1: Data sharing among project’s partners is not always possible • Even under signed NDAs • Different legal advisors do different interpretations of the same Law • GDPR and other data protection rules of EU member states • Health data is very sensitive data that may include patient’s information —so involved people (doctors, IT staff, researchers, industry, etc.) must guarantee no patient’s information is disclosed • Validated/certified anonymization/pseudonymization and de-identification techniques not easy to achieve • No scientific progress without risks • Proposal for addressing this challenge in the Health sector: • Privacy and security by design in the standard protocols to collect and annotate data —protocols to be defined • Legal-technical-ethical committees must be created to issue data-sharing authorisations in order to avoid the responsibility falling on individual doctors or medical teams • Federated and split learning under software restrictions where only training algorithms have access to data
  9. 9. 11 The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111. Challenge 2: Data is not curated enough • Data is partially labelled/annotated • Non-standard protocols were followed to collect datasets for the same diseases • Relevant differences depending on the source/origin (hospital across Europe) • In particular regarding metadata that affects how labels and/or annotations are stored • Proposal for addressing this challenge in the Health sector: • Standard ontologies must be a priority • Standard protocols to collect and store for sharing FAIR data in Europe must be a priority • Make medical personnel more involved in technical projects —difficult to explain
  10. 10. 12 The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111.The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111. Questions? @DeepHealthEU https://deephealth-project.eu Project Coordinator: Mónica Caballero monica.caballero.galeote@everis.com Technical Manager: Jon Ander Gómez jon@upv.es

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