This document outlines four key challenges in medical image diagnostics using artificial intelligence: 1) Data versatility and different peculiarities between patients which can introduce biases. This includes issues like rare diseases with unbalanced data. 2) Difficulties with data collection due to scattered data across countries with different privacy laws, and challenges ensuring high quality labeling. 3) Restrictions of using private medical data through federated learning due to costs and legal issues transferring data. 4) Ensuring AI systems are trustworthy by enabling explainability when models fail, to determine if it is a data or model issue. The goal is to develop laws, technologies and methods like explainable AI to address these challenges.

1. Challenges in Medical Image
Diagnostics
Challenges in Medical Image
Diagnostics
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Data Driven Society
Dr. Sc. Franko Hržić mag. Ing. Comp.
University of Rijeka, Faculty of Engineering, Department of Computer Engineering
University of Rijeka, Center for Artificial Intelligence and Cybersecurity 19.05.2023

2. Sylabus
• Short overview of the field
• Four challenges
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3. Introduction – Typical protocol
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4. Introduction – AI in medicine
• Train machine learning
models that can:
●
Set up diagnoses,
●
Image retrieval,
●
Augment data,
●
…
●
Reduce labor
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5. AI in Medicine: Circle of Life
Hržić, F.; Tschauner, S.; Sorantin, E.; Štajduhar, I. Fracture Recognition
in Paediatric Wrist Radiographs: An Object Detection Approach.
Mathematics 2022, 10, 2939. https://doi.org/10.3390/math10162939
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6. Where is data in AI story
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7. Modern approaches requires
a lot of data
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8. Challenge 1: Data versatility and
different peculiarities
Picture a wedding in your head!
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9. • How about in
medicine?
A wedding in Niyamgiri - India

10. Data versatility and different
peculiarities
• Children are not “small humans”
• Is every disease/injury an anomaly?
• What are the other biases?
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11. Some diseases may be really
Rare – unbalanced data issue
• Dataset provided by Medical University of Graz,
Department of Radiology, Division of Pediatric
Radiology
• Pediatric X-ray images
• GANs as possible solutions?!?
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12. Challenge 1: Solution
Well make datasets publicly
available!!!
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13. Challenge 2: Data collection
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14. Legal challenges regarding AI and
medical data: Scattered data
• To prevent data bias,
data must be collected all over the world.
• Different countries different laws.
• How to export data?
• Patient consent?
• Transfer responsibility on the data provider
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15. Legal challenges regarding AI and
medical data: Data quality
• How to properly label the data?
• The gold standard (occasionally, erroneously, called
the golden standard) is the term used in medicine for
the test (imaging, blood test, biopsy, etc.) that is felt
to be the current best for diagnosis of a particular
condition. The gold standard for any specific disease
is not set in stone and can change over time. It is
against the gold standard that any new diagnostic
test is compared.
• Time consuming? What about images?
Cardoso JR, Pereira LM, Iversen MD, Ramos AL. What is gold standard
and what is ground truth?. (2014) Dental press journal of orthodontics.
19 (5): 27-30. doi:10.1590/2176-9451.19.5.027-030.ebo - Pubmed
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16. Fun fact: We have published the
dataset!
• A pediatric wrist trauma X-ray dataset
(GRAZPEDWRI-DX) for machine learning
• 6,091 patients,
• 10,643 studies (20,327 images)
• 74,459 image tags and features 67,771
labeled objects.
• 2 expert annotators, committee, 3.5
years
Nagy, Eszter, et al. "A pediatric wrist trauma X-ray dataset
(GRAZPEDWRI-DX) for machine learning." Scientific Data 9.1
(2022): 222.
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17. Challenge 2: Solution
• Build platforms for annotating
• Involve medical students (study
has shown they can be usefull :D)
• Make variations of available
annotated data: GAN, Online
learning?
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18. Challenge 3: Federated learning
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We only need models… “Data is
not important.”

19. Private data available through
Federated learning
• Data is not Public.
• Data is not Anonymized!
• Contrastive learning!
• Federated learning, what
is the catch?
Nagy, E., Janisch, M., Hržić, F. et al. A pediatric wrist trauma X-ray
dataset (GRAZPEDWRI-DX) for machine learning. Sci Data 9, 222
(2022). https://doi.org/10.1038/s41597-022-01328-z
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20. Challenge 3: Solution
• Quite possible
• Currently very expensive (Google
Colab: 100 CU = 1Gb/1Hour)
• Many technologies must
collaborate
• Still Law issues
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21. Challenge 4: Trustworthy AI
• Once model is trained – how to
explain why does it fails?
• Data problem or model problem?
Challenge 4: Solution
• Explainable AI
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22. Conclusion
• Data is hard to collect and annotate,
• … even harder to make publicly available,
• Development of laws and supportive
technology
• XAI!
• Go online?
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23. Ultimate goal
Ultimate goal

24. Challenges in Medical Image
Diagnostics
Challenges in Medical Image
Diagnostics
Thank you for your attention!
QUESTION TIME?
University of Rijeka, Faculty of Engineering, Department of Computer Engineering
University of Rijeka, Center for Artificial Intelligence and Cybersecurity