The document outlines the evolution and current state of medical image analysis, retrieval, and evaluation infrastructures, highlighting key projects and challenges in accessing medical imaging data. It emphasizes the importance of data-sharing, quality evaluation of algorithms, and integrating various data sources in the field of medical informatics. Additionally, it discusses future directions aimed at improving data accessibility and research efficiency through advanced computational methods and collaborative efforts.

1. Medical image analysis, retrieval and
evaluation infrastructures
Henning Müller
HES-SO VS &
Martinos Center

2. Overview
• Medical image retrieval projects
• Image analysis and 3D texture modeling
• Data science evaluation infrastructures
– ImageCLEF
– VISCERAL
– EaaS – Evaluation as a Service
• What comes next?

3. Henning Müller
• Studies in medical informatics in
Heidelberg, Germany
– Work in Portland, OR, USA
• PhD in image processingin Geneva,
focus on image analysis and retrieval
– Exchange at Monash Uni., Melbourne, Australia
• Prof titulaire at UNIGE/HUG in medicine (2014)
– Medical image analysis and retrieval for decision
support
• Professor at the HES-SO Valais (2007)
– Head of the eHealth unit
• Sabbaticalat the Martinos Center, Boston, MA

4. Medical image retrieval (history)
• MedGIFT project started in 2002
– Global image similarity
• Texture, grey levels
– Teaching files
– Linking text files and
image similarity
• Often data not available
– Medical data hard to get
– Images and text are
connected in cases
• Unrealistic expectations, high quality vs. browsing
– Semantic gap

5. Medical imaging is big data!!
• Much imaging datais produced
• Imaging data is very complex
– And getting more complex
• Imaging is essential for
diagnosis and treatment
• Images out of their context
loose most of their sense
– Clinical data are necessary
– Diagnoses often not precise
• Evidence-based medicine&
case-basedreasoning

6. Decision support in medicine

7. • Mixing multilingualdata from many resources
and semantic information for medical retrieval
– LinkedLifeData

8. The informed patient

9. Integrated interfaces

10. Texture analysis (2D->3D->4D)
• Describe various medical tissue types
– Brain, lung, …
– Concentration on 3D and 4D data
– Mainly texture descriptors
• Extract visual features/signatures
– Learned, so relation to deep learning
Adrien Depeursinge, Antonio Foncubierta–Rodriguez, Dimitri Van de Ville, and Henning
Müller, Three–dimensional solid texture analysis and retrieval: review and opportunities,
Medical Image Analysis, volume 18, number 1, pages 176-196, 2014.

11. Database with CT image of
interstitial lung diseases
• 128 cases with CT image series and biopsy
confirmed diagnosis
• Manually annotated regions for tissue classes (1946)
– 6 tissue types of 13 with a larger number of examples
• 159 clinical parameters extracted (sparse)
– Smoking history, age, gender,
hematocrit, …
• Availableafter signature of a
license agreement

12. Learned 3D signatures
• Learn combinations of Riesz wavelets as digital
signatures using SVMs (steerable filters)
– Create signatures to detect small local lesions
and visualize them
Adrien Depeursinge, Antonio Foncubierta–Rodriguez, Dimitri Van de Ville, and Henning
Müller, Rotation–covariant feature learning using steerable Riesz wavelets, IEEE
Transactions on Image Processing, volume 23, number 2, page 898-908, 2014.

13. Learning Riesz in 3D
• Most medical tissues are naturally 3D
• But modeling gets much more complex
– Vertical planes
– 3-D checkerboard
– 3-D wiggled
checkerboard

14. Aiding clinical decisions

15. • Benchmark on multimodal imageretrieval
– Run since 2003, medical task since 2004
– Part of the Cross language evaluation forum
• Many tasks related to image retrieval
– Image classification
– Image-based retrieval
– Case-based retrieval
– Compound figure separation
– Caption prediction
– …
• Many old databases remain available, imageclef.org

16. Test

17. Resources available

18. Test DataTraining Data
Participants Organiser
Participant
Virtual
MachinesRegistration
System
Annotation
Management System
Analysis
System
Annotators
(Radiologists)
Locally Installed
Annotation
Clients
Microsoft
Azure
Cloud
Test Data

19. Evaluation as a Service (EaaS)
• Moving the algorithms to the data not vice versa
– Required when data are: very large, changing
quickly, confidential (medical, commercial, …)
• Different approaches
– Source code submission, APIs, VMs local or in the
cloud, Docker containers, specific frameworks
• Allows for continuous evaluation, component-
based evaluation, total reproducibility, updates, …
– Workshop March 2015 in Sierre on EaaS
– Workshop November 2015 in Boston on cloud-
based evaluation

20. Sharing images, research data
• Very important aspect of research is to have solid
methods, data, large if possible
– If data not available, results can not be reproduced
– If data are small, results may be meaningless
• Many multi-center projects spend most money on
data acquisition, often delayed no time for analysis
– IRB takes long, sometimes restrictions are strange
• Research is ineternational!
• NIH & NCI are great to push data availability
– But data can be made available in an unusable way

21. Political support for research
infrastructures!

22. Sustaining biomedical big data

23. Microsoft Azure

24. Intels CCC

25. Institutional support (NCI)
• Using crowdsourcing to link researcher and challenges

26. Business models for these links
• Manually annotate large data sets for challenges
– Data needs to be available in a secure space
• Have researcher work on data (on infrastructure)
– Deliver code
• Commercialize results and share benefits

27. Future of research infrastructures
• Much more centered around data!!
– Nature Scientific Data underlines the importance!
• Data need to be available but in a meaningful way
– Infrastructure needs to be available and way to
evaluate on the data with specific tasks
• More work for data preparation but in line with IRB
– Analysis inside medical insitutions
• Code will become even more portable
– Docker helps enormously and develops quickly
• Public private partnerships to be sustainable
• Total reproducibility, long term, sharing tools
• Much higher efficiency

28. • Part of QIN – Quantitative Imaging Network (NCI)
• Create challenges for QIN to validate tools
• Use Codalabto run project challenges
– Run code in containers (Docker), well integrated
• Automate as much as possible
– Share code blocks across teams, evaluate
combinations

29. Conclusions
• Medicine is (becoming) digital medicine
– More data and more complex links (genes, visual,
signals, …)
• Medical data science requires new infrastructures
– Use routine data, not manually extracted, curated
data, curate large scale, accommodate for errors
– Use large data sets from data warehouses
– Keep data where they are produced
• More “local” computation, so where data are
– Secure aggregation of results
• Sharing infrastructures, data and more

30. Contact
• More information canbe found at
– http://khresmoi.eu/
– http://visceral.eu/
– http://medgift.hevs.ch/
– http://publications.hevs.ch/
• Contact:
– Henning.mueller@hevs.ch