SlideShare a Scribd company logo
1 of 20
Download to read offline
Information Access to Medical Image
  Data: from Big Data to Semantics -
 Academic and Commercial Challenges
Adrien Depeursinge
Henning Müller
Overview
 •    Motivation & objectives
 •    eHealth research at the HES-SO in Sierre
 •    VISCERAL
      •    ETHZ vs. HES-SO
 •    Khresmoi
      •    HES-SO vs. ATOS, Ontotext, (ELDA, HON, GAW)
 •    Conclusions


                                                          2
Motivation for image management
 •  “An image is worth a thousand words”

 •  Medical imaging is estimated 
    to occupy 30% of world 
    storage capacity in 2010!
 •  Mammography data in the 
    US in 2009 amounts to 
    2.5 Petabytes



                              Riding the wave – how Europe can gain from
                              the rising tide of scientific data, report of the
                              European Commission, 10/2010.
                                                                                 3
Objectives of our work
 •    Better exploit visual information in medical
      imaging for decision support
      •    Find similar cases, use these including outcomes for
            diagnosis support
 •    Develop scalable solutions that allow treating
      the volumes produced in hospitals
      •    Detect small regions of interest in medical images
      •    Map images to semantics, store only regions of interest
 •    Link information in reports with image data
      •    Make work of radiologists more efficient
                                                                      4
eHealth at the HES-SO in Sierre
 •    Many eHealth activities since 2007
      •    eHealth unit since 2010
      •    20 persons and three professors
      •    Michael Schumacher, Henning Müller
 •    Several types of projects
      •    EU FP7 projects (Khresmoi, PROMISE, WIDTH,
            VISCERAL, MD-Paedigree, Commodity12, …)
      •    FNS projects (MANY, NinaPro, …)
      •    CTI, Hasler, COST, HES-SO, NanoTerra, mandates

                                                             5
Projet loop in MedGIFT




                          6
Big data challenges and opportunities
 •    Signal data in the images needs to be mapped to
       semantic information
       •    Reduce amount of data to be kept accessible
       •    Get information for decision support
       •    Regions of interest can be extremely small

 •    Simple and efficient tools are required
       •    And these might work better on big data (and need to be
            scalable)

 •    Many rare diseases could be analyzed
       •    These are difficult as people do not know them, they are
            missed and incorrectly treated
       •    Use all data instead of small scale studies
       •    Use data across hospitals, quality is important
                                                                      7
VISCERAL
•    EU funded project (2012-2015)
     •    HES-SO, ETHZ, UHD, MUW, TUW, Gencat
     •    Coordination action, so not research in itself
•    Organize competitions on medical 
     image analysis on big data (10-40 TB)
     •    All computation done in the cloud, 
          collaboration with Microsoft
     •    Identifying landmarks in the body
     •    Finding similar cases
•    Annotation by medical doctors
                                                            8
Objectives of VISCERAL
 •    Create a cloud-based infrastructure to test
      algorithms on big and potentially confidential
      data
 •    Annotate large amounts of medical image data
      for system evaluation (annotate once, reuse)
      •    Annotation in Hungary to keep costs limited
      •    3D annotation and labels in the RadLex terminology
 •    Support the coordination of research work on
       relevant objectives in medical imaging
      •    Including academic groups and companies such as
            Microsoft, Siemens, Toshiba, etc.
                                                                 9
Evaluations in VISCERAL
Test




                           10
KHRESMOI
 •  4 year, 10’000’000 € budget




                                   11
Khresmoi goals
•    Trustable information adapted to each user group
     •    All tools as open source
•    Extract semantic information from all sources
     •    LinkedLifeData




                                                      12
Current status
 •    Project at the beginning of year 3 of 4 years
      •    Half-time
 •    User tests have started among the three user
      groups (much feedback on prototypes expected)
      •    Different types of interfaces
      •    Eye tracking
 •    Implement changes
      to adapt to the user
      groups

                                                       13
Software architecture




                         14
User interfaces




                   15
User tests




              16
Public/private  academic collaborations
•    Close collaboration between actors is beneficial
     •    Different view points on the same problems
     •    Different ways of being evaluated (publications, projects, $)
     •    For larger projects the best partners are necessary
•    Interdisciplinary work is enriching
     •    Creates new ideas (and sometimes frustrations)
     •    Is needed in most fields of computer science
•    Innovation is often the goal of funding
     •    HES has developers, PhD students and senior researcher
          collaborating on the same problems
                                                                      17
Eye tracking
•  http://www.youtube.com/watch?v=YWo1Cx3jdOo




                                             18
Demo
 •  www.youtube.com/watch?v=cMoONC0Tz2c




                                           19
Questions?
 •    More information can be found at 
      •    http://medgift.hevs.ch/
      •    http://publications.hevs.ch/
      •    http://khresmoi.eu/
      •    http://visceral.eu/


 •    Contact:
      •    Henning.mueller@hevs.ch


                                           20

More Related Content

What's hot

The FindMeEvidence project: An open-source, mobile-friendly search engine for...
The FindMeEvidence project: An open-source, mobile-friendly search engine for...The FindMeEvidence project: An open-source, mobile-friendly search engine for...
The FindMeEvidence project: An open-source, mobile-friendly search engine for...Matthias Samwald
 
Vector spaces for information extraction - Random Projection Example
Vector spaces for information extraction - Random Projection ExampleVector spaces for information extraction - Random Projection Example
Vector spaces for information extraction - Random Projection Examplenet2-project
 
The culture of researchData
The culture of researchDataThe culture of researchData
The culture of researchDatapetermurrayrust
 
Open Access Developments in Europe, Sept 2014
Open Access Developments in Europe, Sept 2014Open Access Developments in Europe, Sept 2014
Open Access Developments in Europe, Sept 2014SPARC Europe
 
Open by default: the challenges of research data in Europe
Open by default: the challenges of research data in EuropeOpen by default: the challenges of research data in Europe
Open by default: the challenges of research data in EuropeJean-François Dechamp
 
Adopting a situated learning framework for (big) data projects
Adopting a situated learning framework for (big) data projectsAdopting a situated learning framework for (big) data projects
Adopting a situated learning framework for (big) data projectsCranfield University
 
EGU GA 2018 OSGeo Townhall
EGU GA 2018 OSGeo TownhallEGU GA 2018 OSGeo Townhall
EGU GA 2018 OSGeo TownhallPeter Löwe
 

What's hot (9)

The FindMeEvidence project: An open-source, mobile-friendly search engine for...
The FindMeEvidence project: An open-source, mobile-friendly search engine for...The FindMeEvidence project: An open-source, mobile-friendly search engine for...
The FindMeEvidence project: An open-source, mobile-friendly search engine for...
 
Vector spaces for information extraction - Random Projection Example
Vector spaces for information extraction - Random Projection ExampleVector spaces for information extraction - Random Projection Example
Vector spaces for information extraction - Random Projection Example
 
Katarzyna Szkuta: "The European Open Science Cloud and the Open Science Policy"
Katarzyna Szkuta: "The European Open Science Cloud and the Open Science Policy"Katarzyna Szkuta: "The European Open Science Cloud and the Open Science Policy"
Katarzyna Szkuta: "The European Open Science Cloud and the Open Science Policy"
 
201201 19 gold oa (dechamp)
201201 19 gold oa (dechamp)201201 19 gold oa (dechamp)
201201 19 gold oa (dechamp)
 
The culture of researchData
The culture of researchDataThe culture of researchData
The culture of researchData
 
Open Access Developments in Europe, Sept 2014
Open Access Developments in Europe, Sept 2014Open Access Developments in Europe, Sept 2014
Open Access Developments in Europe, Sept 2014
 
Open by default: the challenges of research data in Europe
Open by default: the challenges of research data in EuropeOpen by default: the challenges of research data in Europe
Open by default: the challenges of research data in Europe
 
Adopting a situated learning framework for (big) data projects
Adopting a situated learning framework for (big) data projectsAdopting a situated learning framework for (big) data projects
Adopting a situated learning framework for (big) data projects
 
EGU GA 2018 OSGeo Townhall
EGU GA 2018 OSGeo TownhallEGU GA 2018 OSGeo Townhall
EGU GA 2018 OSGeo Townhall
 

Viewers also liked (7)

Process to divest therapeutic pipelines
Process to divest therapeutic pipelinesProcess to divest therapeutic pipelines
Process to divest therapeutic pipelines
 
Evaluation q4
Evaluation   q4Evaluation   q4
Evaluation q4
 
Fairtrace - Tracing the textile industry
Fairtrace - Tracing the textile industryFairtrace - Tracing the textile industry
Fairtrace - Tracing the textile industry
 
U3 presentation 1
U3 presentation 1U3 presentation 1
U3 presentation 1
 
Monitoring Gestational Diabetes
Monitoring Gestational DiabetesMonitoring Gestational Diabetes
Monitoring Gestational Diabetes
 
Managing ict final
Managing ict finalManaging ict final
Managing ict final
 
La eHealth en général et quelques projets de la HES-SO Valais
La eHealth en général et quelques projets de la HES-SO ValaisLa eHealth en général et quelques projets de la HES-SO Valais
La eHealth en général et quelques projets de la HES-SO Valais
 

Similar to Information Access to Medical Image Data: from Big Data to Semantics - Academic and Commercial Challenges

Henning Müller et Michael Schumacher pour la journée e-health 2013
Henning Müller et Michael Schumacher pour la journée e-health 2013Henning Müller et Michael Schumacher pour la journée e-health 2013
Henning Müller et Michael Schumacher pour la journée e-health 2013Thearkvalais
 
Social Networks and Collaborative Platforms for Data Sharing in Radiology
Social Networks and Collaborative Platforms for Data Sharing in RadiologySocial Networks and Collaborative Platforms for Data Sharing in Radiology
Social Networks and Collaborative Platforms for Data Sharing in RadiologyErik R. Ranschaert, MD, PhD
 
eROSA Stakeholder WS1: The European Open Science Cloud: Vision & state of play
eROSA Stakeholder WS1: The European Open Science Cloud: Vision & state of playeROSA Stakeholder WS1: The European Open Science Cloud: Vision & state of play
eROSA Stakeholder WS1: The European Open Science Cloud: Vision & state of playe-ROSA
 
Australia's Environmental Predictive Capability
Australia's Environmental Predictive CapabilityAustralia's Environmental Predictive Capability
Australia's Environmental Predictive CapabilityTERN Australia
 
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and RealityA VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality Paul Courtney
 
Pistoia alliance debates analytics 15-09-2015 16.00
Pistoia alliance debates   analytics 15-09-2015 16.00Pistoia alliance debates   analytics 15-09-2015 16.00
Pistoia alliance debates analytics 15-09-2015 16.00Pistoia Alliance
 
1st eStandards conference: next steps for standardization in large scale eHea...
1st eStandards conference: next steps for standardization in large scale eHea...1st eStandards conference: next steps for standardization in large scale eHea...
1st eStandards conference: next steps for standardization in large scale eHea...chronaki
 
Tragedy of the Data Commons (ODSC-East, 2021)
Tragedy of the Data Commons (ODSC-East, 2021)Tragedy of the Data Commons (ODSC-East, 2021)
Tragedy of the Data Commons (ODSC-East, 2021)James Hendler
 
Abridged résumé for quick review
Abridged résumé for quick reviewAbridged résumé for quick review
Abridged résumé for quick reviewFrederic Perez
 
160905 tryggve-at-eccb pursula
160905 tryggve-at-eccb pursula160905 tryggve-at-eccb pursula
160905 tryggve-at-eccb pursulaanttipursula
 
Tragedy of the (Data) Commons
Tragedy of the (Data) CommonsTragedy of the (Data) Commons
Tragedy of the (Data) CommonsJames Hendler
 
Data discovery and sharing at UCLH
Data discovery and sharing at UCLHData discovery and sharing at UCLH
Data discovery and sharing at UCLHJisc
 
CODATA International Training Workshop in Big Data for Science for Researcher...
CODATA International Training Workshop in Big Data for Science for Researcher...CODATA International Training Workshop in Big Data for Science for Researcher...
CODATA International Training Workshop in Big Data for Science for Researcher...Johann van Wyk
 

Similar to Information Access to Medical Image Data: from Big Data to Semantics - Academic and Commercial Challenges (20)

eHealth projects in Sierre – Khresmoi
eHealth projects in Sierre – KhresmoieHealth projects in Sierre – Khresmoi
eHealth projects in Sierre – Khresmoi
 
eHealth unit HES-SO in Sierre
eHealth unit HES-SO in SierreeHealth unit HES-SO in Sierre
eHealth unit HES-SO in Sierre
 
Henning Müller et Michael Schumacher pour la journée e-health 2013
Henning Müller et Michael Schumacher pour la journée e-health 2013Henning Müller et Michael Schumacher pour la journée e-health 2013
Henning Müller et Michael Schumacher pour la journée e-health 2013
 
Medical image analysis and big data evaluation infrastructures
Medical image analysis and big data evaluation infrastructuresMedical image analysis and big data evaluation infrastructures
Medical image analysis and big data evaluation infrastructures
 
Social Networks and Collaborative Platforms for Data Sharing in Radiology
Social Networks and Collaborative Platforms for Data Sharing in RadiologySocial Networks and Collaborative Platforms for Data Sharing in Radiology
Social Networks and Collaborative Platforms for Data Sharing in Radiology
 
Medical image analysis, retrieval and evaluation infrastructures
Medical image analysis, retrieval and evaluation infrastructuresMedical image analysis, retrieval and evaluation infrastructures
Medical image analysis, retrieval and evaluation infrastructures
 
MedGIFT projects in medical imaging
MedGIFT projects in medical imagingMedGIFT projects in medical imaging
MedGIFT projects in medical imaging
 
eROSA Stakeholder WS1: The European Open Science Cloud: Vision & state of play
eROSA Stakeholder WS1: The European Open Science Cloud: Vision & state of playeROSA Stakeholder WS1: The European Open Science Cloud: Vision & state of play
eROSA Stakeholder WS1: The European Open Science Cloud: Vision & state of play
 
Australia's Environmental Predictive Capability
Australia's Environmental Predictive CapabilityAustralia's Environmental Predictive Capability
Australia's Environmental Predictive Capability
 
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and RealityA VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
 
Pistoia alliance debates analytics 15-09-2015 16.00
Pistoia alliance debates   analytics 15-09-2015 16.00Pistoia alliance debates   analytics 15-09-2015 16.00
Pistoia alliance debates analytics 15-09-2015 16.00
 
1st eStandards conference: next steps for standardization in large scale eHea...
1st eStandards conference: next steps for standardization in large scale eHea...1st eStandards conference: next steps for standardization in large scale eHea...
1st eStandards conference: next steps for standardization in large scale eHea...
 
Tragedy of the Data Commons (ODSC-East, 2021)
Tragedy of the Data Commons (ODSC-East, 2021)Tragedy of the Data Commons (ODSC-East, 2021)
Tragedy of the Data Commons (ODSC-East, 2021)
 
2015 04-18-wilson cg
2015 04-18-wilson cg2015 04-18-wilson cg
2015 04-18-wilson cg
 
Abridged résumé for quick review
Abridged résumé for quick reviewAbridged résumé for quick review
Abridged résumé for quick review
 
160905 tryggve-at-eccb pursula
160905 tryggve-at-eccb pursula160905 tryggve-at-eccb pursula
160905 tryggve-at-eccb pursula
 
Tragedy of the (Data) Commons
Tragedy of the (Data) CommonsTragedy of the (Data) Commons
Tragedy of the (Data) Commons
 
Data discovery and sharing at UCLH
Data discovery and sharing at UCLHData discovery and sharing at UCLH
Data discovery and sharing at UCLH
 
Hadoop Enabled Healthcare
Hadoop Enabled HealthcareHadoop Enabled Healthcare
Hadoop Enabled Healthcare
 
CODATA International Training Workshop in Big Data for Science for Researcher...
CODATA International Training Workshop in Big Data for Science for Researcher...CODATA International Training Workshop in Big Data for Science for Researcher...
CODATA International Training Workshop in Big Data for Science for Researcher...
 

More from Institute of Information Systems (HES-SO)

Classification of noisy free-text prostate cancer pathology reports using nat...
Classification of noisy free-text prostate cancer pathology reports using nat...Classification of noisy free-text prostate cancer pathology reports using nat...
Classification of noisy free-text prostate cancer pathology reports using nat...Institute of Information Systems (HES-SO)
 
Machine learning assisted citation screening for Systematic Reviews - Anjani ...
Machine learning assisted citation screening for Systematic Reviews - Anjani ...Machine learning assisted citation screening for Systematic Reviews - Anjani ...
Machine learning assisted citation screening for Systematic Reviews - Anjani ...Institute of Information Systems (HES-SO)
 
Exploiting biomedical literature to mine out a large multimodal dataset of ra...
Exploiting biomedical literature to mine out a large multimodal dataset of ra...Exploiting biomedical literature to mine out a large multimodal dataset of ra...
Exploiting biomedical literature to mine out a large multimodal dataset of ra...Institute of Information Systems (HES-SO)
 
Studying Public Medical Images from Open Access Literature and Social Network...
Studying Public Medical Images from Open Access Literature and Social Network...Studying Public Medical Images from Open Access Literature and Social Network...
Studying Public Medical Images from Open Access Literature and Social Network...Institute of Information Systems (HES-SO)
 
Risques opérationnels et le système de contrôle interne : les limites d’un te...
Risques opérationnels et le système de contrôle interne : les limites d’un te...Risques opérationnels et le système de contrôle interne : les limites d’un te...
Risques opérationnels et le système de contrôle interne : les limites d’un te...Institute of Information Systems (HES-SO)
 
Le contrôle interne dans les administrations publiques tient-il toutes ses pr...
Le contrôle interne dans les administrations publiques tient-il toutes ses pr...Le contrôle interne dans les administrations publiques tient-il toutes ses pr...
Le contrôle interne dans les administrations publiques tient-il toutes ses pr...Institute of Information Systems (HES-SO)
 
Le système de contrôle interne : Présentation générale, enjeux et méthodes
Le système de contrôle interne : Présentation générale, enjeux et méthodesLe système de contrôle interne : Présentation générale, enjeux et méthodes
Le système de contrôle interne : Présentation générale, enjeux et méthodesInstitute of Information Systems (HES-SO)
 
A 3-D Riesz-Covariance Texture Model for the Prediction of Nodule Recurrence ...
A 3-D Riesz-Covariance Texture Model for the Prediction of Nodule Recurrence ...A 3-D Riesz-Covariance Texture Model for the Prediction of Nodule Recurrence ...
A 3-D Riesz-Covariance Texture Model for the Prediction of Nodule Recurrence ...Institute of Information Systems (HES-SO)
 
NOSE: une approche Smart-City pour les zones périphériques et extra-urbaines
NOSE: une approche Smart-City pour les zones périphériques et extra-urbainesNOSE: une approche Smart-City pour les zones périphériques et extra-urbaines
NOSE: une approche Smart-City pour les zones périphériques et extra-urbainesInstitute of Information Systems (HES-SO)
 

More from Institute of Information Systems (HES-SO) (20)

MIE20232.pptx
MIE20232.pptxMIE20232.pptx
MIE20232.pptx
 
Classification of noisy free-text prostate cancer pathology reports using nat...
Classification of noisy free-text prostate cancer pathology reports using nat...Classification of noisy free-text prostate cancer pathology reports using nat...
Classification of noisy free-text prostate cancer pathology reports using nat...
 
Machine learning assisted citation screening for Systematic Reviews - Anjani ...
Machine learning assisted citation screening for Systematic Reviews - Anjani ...Machine learning assisted citation screening for Systematic Reviews - Anjani ...
Machine learning assisted citation screening for Systematic Reviews - Anjani ...
 
Exploiting biomedical literature to mine out a large multimodal dataset of ra...
Exploiting biomedical literature to mine out a large multimodal dataset of ra...Exploiting biomedical literature to mine out a large multimodal dataset of ra...
Exploiting biomedical literature to mine out a large multimodal dataset of ra...
 
L'IoT dans les usines. Quels avantages ?
L'IoT dans les usines. Quels avantages ?L'IoT dans les usines. Quels avantages ?
L'IoT dans les usines. Quels avantages ?
 
Studying Public Medical Images from Open Access Literature and Social Network...
Studying Public Medical Images from Open Access Literature and Social Network...Studying Public Medical Images from Open Access Literature and Social Network...
Studying Public Medical Images from Open Access Literature and Social Network...
 
Risques opérationnels et le système de contrôle interne : les limites d’un te...
Risques opérationnels et le système de contrôle interne : les limites d’un te...Risques opérationnels et le système de contrôle interne : les limites d’un te...
Risques opérationnels et le système de contrôle interne : les limites d’un te...
 
Le contrôle interne dans les administrations publiques tient-il toutes ses pr...
Le contrôle interne dans les administrations publiques tient-il toutes ses pr...Le contrôle interne dans les administrations publiques tient-il toutes ses pr...
Le contrôle interne dans les administrations publiques tient-il toutes ses pr...
 
Le système de contrôle interne : Présentation générale, enjeux et méthodes
Le système de contrôle interne : Présentation générale, enjeux et méthodesLe système de contrôle interne : Présentation générale, enjeux et méthodes
Le système de contrôle interne : Présentation générale, enjeux et méthodes
 
Crowdsourcing-based Mobile Application for Wheelchair Accessibility
Crowdsourcing-based Mobile Application for Wheelchair AccessibilityCrowdsourcing-based Mobile Application for Wheelchair Accessibility
Crowdsourcing-based Mobile Application for Wheelchair Accessibility
 
Quelle(s) valeur(s) pour le leadership stratégique ?
Quelle(s) valeur(s) pour le leadership stratégique ?Quelle(s) valeur(s) pour le leadership stratégique ?
Quelle(s) valeur(s) pour le leadership stratégique ?
 
A 3-D Riesz-Covariance Texture Model for the Prediction of Nodule Recurrence ...
A 3-D Riesz-Covariance Texture Model for the Prediction of Nodule Recurrence ...A 3-D Riesz-Covariance Texture Model for the Prediction of Nodule Recurrence ...
A 3-D Riesz-Covariance Texture Model for the Prediction of Nodule Recurrence ...
 
Challenges in medical imaging and the VISCERAL model
Challenges in medical imaging and the VISCERAL modelChallenges in medical imaging and the VISCERAL model
Challenges in medical imaging and the VISCERAL model
 
NOSE: une approche Smart-City pour les zones périphériques et extra-urbaines
NOSE: une approche Smart-City pour les zones périphériques et extra-urbainesNOSE: une approche Smart-City pour les zones périphériques et extra-urbaines
NOSE: une approche Smart-City pour les zones périphériques et extra-urbaines
 
How to detect soft falls on devices
How to detect soft falls on devicesHow to detect soft falls on devices
How to detect soft falls on devices
 
FUNDAMENTALS OF TEXTURE PROCESSING FOR BIOMEDICAL IMAGE ANALYSIS
FUNDAMENTALS OF TEXTURE PROCESSING FOR BIOMEDICAL IMAGE ANALYSISFUNDAMENTALS OF TEXTURE PROCESSING FOR BIOMEDICAL IMAGE ANALYSIS
FUNDAMENTALS OF TEXTURE PROCESSING FOR BIOMEDICAL IMAGE ANALYSIS
 
MOBILE COLLECTION AND DISSEMINATION OF SENIORS’ SKILLS
MOBILE COLLECTION AND DISSEMINATION OF SENIORS’ SKILLSMOBILE COLLECTION AND DISSEMINATION OF SENIORS’ SKILLS
MOBILE COLLECTION AND DISSEMINATION OF SENIORS’ SKILLS
 
Enhanced Students Laboratory The GET project
Enhanced Students Laboratory The GET projectEnhanced Students Laboratory The GET project
Enhanced Students Laboratory The GET project
 
Solar production prediction based on non linear meteo source adaptation
Solar production prediction based on non linear meteo source adaptationSolar production prediction based on non linear meteo source adaptation
Solar production prediction based on non linear meteo source adaptation
 
Exploring the New Trends of Chinese Tourists in Switzerland
Exploring the New Trends of Chinese Tourists in SwitzerlandExploring the New Trends of Chinese Tourists in Switzerland
Exploring the New Trends of Chinese Tourists in Switzerland
 

Recently uploaded

QMMS Lesson 2 - Using MS Excel Formula.pdf
QMMS Lesson 2 - Using MS Excel Formula.pdfQMMS Lesson 2 - Using MS Excel Formula.pdf
QMMS Lesson 2 - Using MS Excel Formula.pdfROWELL MARQUINA
 
A Glance At The Java Performance Toolbox
A Glance At The Java Performance ToolboxA Glance At The Java Performance Toolbox
A Glance At The Java Performance ToolboxAna-Maria Mihalceanu
 
Dynamical Context introduction word sensibility orientation
Dynamical Context introduction word sensibility orientationDynamical Context introduction word sensibility orientation
Dynamical Context introduction word sensibility orientationBuild Intuit
 
Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsPotential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsRavi Sanghani
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Hiroshi SHIBATA
 
Transport in Open Pits______SM_MI10415MI
Transport in Open Pits______SM_MI10415MITransport in Open Pits______SM_MI10415MI
Transport in Open Pits______SM_MI10415MIRomil Mishra
 
HCI Lesson 1 - Introduction to Human-Computer Interaction.pdf
HCI Lesson 1 - Introduction to Human-Computer Interaction.pdfHCI Lesson 1 - Introduction to Human-Computer Interaction.pdf
HCI Lesson 1 - Introduction to Human-Computer Interaction.pdfROWELL MARQUINA
 
Arti Languages Pre Seed Pitchdeck 2024.pdf
Arti Languages Pre Seed Pitchdeck 2024.pdfArti Languages Pre Seed Pitchdeck 2024.pdf
Arti Languages Pre Seed Pitchdeck 2024.pdfwill854175
 
Introduction-to-Wazuh-and-its-integration.pptx
Introduction-to-Wazuh-and-its-integration.pptxIntroduction-to-Wazuh-and-its-integration.pptx
Introduction-to-Wazuh-and-its-integration.pptxmprakaash5
 
All These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFAll These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFMichael Gough
 
Women in Automation 2024: Career session - explore career paths in automation
Women in Automation 2024: Career session - explore career paths in automationWomen in Automation 2024: Career session - explore career paths in automation
Women in Automation 2024: Career session - explore career paths in automationDianaGray10
 
Landscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdfLandscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdfAarwolf Industries LLC
 
Green paths: Learning from publishers’ sustainability journeys - Tech Forum 2024
Green paths: Learning from publishers’ sustainability journeys - Tech Forum 2024Green paths: Learning from publishers’ sustainability journeys - Tech Forum 2024
Green paths: Learning from publishers’ sustainability journeys - Tech Forum 2024BookNet Canada
 
Transcript: Green paths: Learning from publishers’ sustainability journeys - ...
Transcript: Green paths: Learning from publishers’ sustainability journeys - ...Transcript: Green paths: Learning from publishers’ sustainability journeys - ...
Transcript: Green paths: Learning from publishers’ sustainability journeys - ...BookNet Canada
 
Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Farhan Tariq
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch TuesdayIvanti
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesThousandEyes
 
Automation Ops Series: Session 3 - Solutions management
Automation Ops Series: Session 3 - Solutions managementAutomation Ops Series: Session 3 - Solutions management
Automation Ops Series: Session 3 - Solutions managementDianaGray10
 
Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024TopCSSGallery
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfNeo4j
 

Recently uploaded (20)

QMMS Lesson 2 - Using MS Excel Formula.pdf
QMMS Lesson 2 - Using MS Excel Formula.pdfQMMS Lesson 2 - Using MS Excel Formula.pdf
QMMS Lesson 2 - Using MS Excel Formula.pdf
 
A Glance At The Java Performance Toolbox
A Glance At The Java Performance ToolboxA Glance At The Java Performance Toolbox
A Glance At The Java Performance Toolbox
 
Dynamical Context introduction word sensibility orientation
Dynamical Context introduction word sensibility orientationDynamical Context introduction word sensibility orientation
Dynamical Context introduction word sensibility orientation
 
Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsPotential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and Insights
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024
 
Transport in Open Pits______SM_MI10415MI
Transport in Open Pits______SM_MI10415MITransport in Open Pits______SM_MI10415MI
Transport in Open Pits______SM_MI10415MI
 
HCI Lesson 1 - Introduction to Human-Computer Interaction.pdf
HCI Lesson 1 - Introduction to Human-Computer Interaction.pdfHCI Lesson 1 - Introduction to Human-Computer Interaction.pdf
HCI Lesson 1 - Introduction to Human-Computer Interaction.pdf
 
Arti Languages Pre Seed Pitchdeck 2024.pdf
Arti Languages Pre Seed Pitchdeck 2024.pdfArti Languages Pre Seed Pitchdeck 2024.pdf
Arti Languages Pre Seed Pitchdeck 2024.pdf
 
Introduction-to-Wazuh-and-its-integration.pptx
Introduction-to-Wazuh-and-its-integration.pptxIntroduction-to-Wazuh-and-its-integration.pptx
Introduction-to-Wazuh-and-its-integration.pptx
 
All These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFAll These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDF
 
Women in Automation 2024: Career session - explore career paths in automation
Women in Automation 2024: Career session - explore career paths in automationWomen in Automation 2024: Career session - explore career paths in automation
Women in Automation 2024: Career session - explore career paths in automation
 
Landscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdfLandscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdf
 
Green paths: Learning from publishers’ sustainability journeys - Tech Forum 2024
Green paths: Learning from publishers’ sustainability journeys - Tech Forum 2024Green paths: Learning from publishers’ sustainability journeys - Tech Forum 2024
Green paths: Learning from publishers’ sustainability journeys - Tech Forum 2024
 
Transcript: Green paths: Learning from publishers’ sustainability journeys - ...
Transcript: Green paths: Learning from publishers’ sustainability journeys - ...Transcript: Green paths: Learning from publishers’ sustainability journeys - ...
Transcript: Green paths: Learning from publishers’ sustainability journeys - ...
 
Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch Tuesday
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
 
Automation Ops Series: Session 3 - Solutions management
Automation Ops Series: Session 3 - Solutions managementAutomation Ops Series: Session 3 - Solutions management
Automation Ops Series: Session 3 - Solutions management
 
Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdf
 

Information Access to Medical Image Data: from Big Data to Semantics - Academic and Commercial Challenges

  • 1. Information Access to Medical Image Data: from Big Data to Semantics - Academic and Commercial Challenges Adrien Depeursinge Henning Müller
  • 2. Overview •  Motivation & objectives •  eHealth research at the HES-SO in Sierre •  VISCERAL •  ETHZ vs. HES-SO •  Khresmoi •  HES-SO vs. ATOS, Ontotext, (ELDA, HON, GAW) •  Conclusions 2
  • 3. Motivation for image management •  “An image is worth a thousand words” •  Medical imaging is estimated to occupy 30% of world storage capacity in 2010! •  Mammography data in the US in 2009 amounts to 2.5 Petabytes Riding the wave – how Europe can gain from the rising tide of scientific data, report of the European Commission, 10/2010. 3
  • 4. Objectives of our work •  Better exploit visual information in medical imaging for decision support •  Find similar cases, use these including outcomes for diagnosis support •  Develop scalable solutions that allow treating the volumes produced in hospitals •  Detect small regions of interest in medical images •  Map images to semantics, store only regions of interest •  Link information in reports with image data •  Make work of radiologists more efficient 4
  • 5. eHealth at the HES-SO in Sierre •  Many eHealth activities since 2007 •  eHealth unit since 2010 •  20 persons and three professors •  Michael Schumacher, Henning Müller •  Several types of projects •  EU FP7 projects (Khresmoi, PROMISE, WIDTH, VISCERAL, MD-Paedigree, Commodity12, …) •  FNS projects (MANY, NinaPro, …) •  CTI, Hasler, COST, HES-SO, NanoTerra, mandates 5
  • 6. Projet loop in MedGIFT 6
  • 7. Big data challenges and opportunities •  Signal data in the images needs to be mapped to semantic information •  Reduce amount of data to be kept accessible •  Get information for decision support •  Regions of interest can be extremely small •  Simple and efficient tools are required •  And these might work better on big data (and need to be scalable) •  Many rare diseases could be analyzed •  These are difficult as people do not know them, they are missed and incorrectly treated •  Use all data instead of small scale studies •  Use data across hospitals, quality is important 7
  • 8. VISCERAL •  EU funded project (2012-2015) •  HES-SO, ETHZ, UHD, MUW, TUW, Gencat •  Coordination action, so not research in itself •  Organize competitions on medical image analysis on big data (10-40 TB) •  All computation done in the cloud, collaboration with Microsoft •  Identifying landmarks in the body •  Finding similar cases •  Annotation by medical doctors 8
  • 9. Objectives of VISCERAL •  Create a cloud-based infrastructure to test algorithms on big and potentially confidential data •  Annotate large amounts of medical image data for system evaluation (annotate once, reuse) •  Annotation in Hungary to keep costs limited •  3D annotation and labels in the RadLex terminology •  Support the coordination of research work on relevant objectives in medical imaging •  Including academic groups and companies such as Microsoft, Siemens, Toshiba, etc. 9
  • 11. KHRESMOI •  4 year, 10’000’000 € budget 11
  • 12. Khresmoi goals •  Trustable information adapted to each user group •  All tools as open source •  Extract semantic information from all sources •  LinkedLifeData 12
  • 13. Current status •  Project at the beginning of year 3 of 4 years •  Half-time •  User tests have started among the three user groups (much feedback on prototypes expected) •  Different types of interfaces •  Eye tracking •  Implement changes to adapt to the user groups 13
  • 17. Public/private academic collaborations •  Close collaboration between actors is beneficial •  Different view points on the same problems •  Different ways of being evaluated (publications, projects, $) •  For larger projects the best partners are necessary •  Interdisciplinary work is enriching •  Creates new ideas (and sometimes frustrations) •  Is needed in most fields of computer science •  Innovation is often the goal of funding •  HES has developers, PhD students and senior researcher collaborating on the same problems 17
  • 20. Questions? •  More information can be found at •  http://medgift.hevs.ch/ •  http://publications.hevs.ch/ •  http://khresmoi.eu/ •  http://visceral.eu/ •  Contact: •  Henning.mueller@hevs.ch 20