This document discusses the utilization of public medical images from open access literature and social networks for enhancing deep learning model training in image analysis. It highlights the challenges of data diversity, class imbalances in rare diseases, and the need for effective data aggregation and filtering techniques. The article also emphasizes the advantages of using diverse and rare images from various sources alongside next steps for improving data curation and machine learning tasks.

1. Studying Public Medical Images from the Open Access
Literature and Social Networks for Model Training and
Knowledge Extraction
Vincent Andrearczyk
HES-SO, Switzerland
MMM 2020, 08.01.2020
Henning Müller, Vincent Andrearczyk, Oscar Jimenez, Anjani Dhrangadhariya,
Roger Schaer, and Manfredo Atzori

2. Motivation
• Deep learning has been a driving force for
improving many applications of image analysis
• Complex networks require large amounts of
training data
- Data diversity is important for generalizability
• Most medical data sets have strong class
imbalances (rare diseases)
- Rare diseases require data from multiple centers
making the organization complex
• Many resources that include images have become
available in the past few years
- PubMed Central, TCIA, social networks, etc.

3. Objectives of this article
• Summarize existing approaches that harvest
public data
– Focusing on PubMed Central and social networks
• Highlight advantages and difficulties in exploiting
the data
– (+) Very diverse data
– (+) Rare cases are oversampled
– (-) Much pre-treatment and filtering is required
• Develop next steps required to fully use the data

4. PubMed Central
• Repository with the biomedical open access
literature, including images as files, etc.
– 3-4 images per article,

5. PubMed Central
• Repository with the biomedical open access
literature, including images as files, etc.
– 3-4 images per article,
– increasing # articles

6. Methodology for finding articles
• Analysis of tasks of ImageCLEF and work done
on these tasks using data from ImageCLEF
– Over the past 12 years
– Steps of filtering out data taken from this
• Use of Google scholar to add references
– Terms “medical image classification”, “publicly
accessible resources”, “medical literature”,
“machine learning” were combined
• Dynamically growing data sets were favored
• Journal papers were referenced over
conference publication

7. Image retrieval
• Allows to search for images with text
– Or semantic terms such as UMLS or MeSH
• Content-based image retrieval
Demner-Fushman, et al. (2012), Journal of Computing Science and Engineering

8. Structuring the visual content
• Define types of images to make the literature
images classifiable
– Extremely large variety in most categories
– Many sub-categories are possible
– Categories with clinical relevance
are most important
– Allows removing noise
– Compound figures
are separately treated
[ImageCLEF 2013]

9. Challenges in the data
• Look-alikes
– Much strange content that needs to be removed

10. Challenges in the data
• Look-alikes
– Much strange content that needs to be removed
• Compound figures can not easily be classified,
as they may contain aspects of several classes
– Cutting them into subfigures makes content
accessible

11. Meta data available for PMC
• Text of the figure caption
– Relatively specific but often short
– Hard for compound figures that contain many parts
• Full text of the article
– Non specific for individual figures
– Location of the figure is available
• Article title and author-generated key words
• Global MeSH terms (Manually attached)
– Cover species and organs
• Not all is available for all articles (incomplete)

12. Tasks to make figures accessible
• Removing very small images & strange aspect
ratios
• Classify figures into figure types
– Using image data and also text
– Remove non-relevant images, e.g. flowcharts
• Detect and cut compound figures into their parts
– Classify these into figure types again
• Filter human and animal tissue
• Filter specific organs of interest
• Find diseases or grading/staging
– Ground truth classes for machine learning

13. Advantages of literature images
• Rare images are generally used for articles and
case descriptions
– Mostly extreme cases to share the knowledge
on them
– Creates critical mass for rare diseases
• Images are from many laboratories and thus
contain many image variations
– Increase generalizability of learned models
• Exponentially increasing content

14. Problems with filtered images
• Many images might be missed by automatic
filtering
• Ground truth is not always solid
• Images might not have clinical quality
– Grey level resolution
– No information on level/window setting
– Cropped images, arrows in images, other overlays
• Size of the images is often small for publications
• Scale of images is not known (can be detected)
Otalora et al. (2018) MICCAI 2018

15. An example of Twitter images
• Images and information posted by pathologists on
Twitter
• Create dataset of histopathology images
• Train machine learning algorithms
– identify stains (H&E, IHC ...)
– discriminate between different tissues
– predict malignant tumors
• Limitations:
– good results (AUROC 0.9) only for simple tasks: H&E
vs rest
Schaumberg et al. (2018), BioRxiv

16. Next steps
• Quickly increasing content offers many possibilities
– Automatic pipelines need to contain update
mechanisms based on latest imaging equipment
– Community efforts for data curation
• Distribute the class labels with confidence scores
via PMC
• Evaluate impact on machine learning tasks of
adding such diverse sources

17. Next steps
• We have been working on it!
– Mined out 32,486 light microscopy human rare
cancer images Dhrangadhariya et al. (2020) SPIE2020
– Automatic generalizable filtering pipeline
In preparation: Jimenez et al. (2020) Journal of the American Medical Informatics Association
– Benefits in deep learning clinical tasks … to come

18. Conclusions
• Images from public resources are complementary to
clinical images for machine learning
– Rare cases, much diversity
– Very large amount of data
• How can we obtain high quality annotations with
limited effort (for example via active learning)

19. Contact
• More information can be found at
– http://medgift.hevs.ch/
– http://publications.hevs.ch
• Contact:
– vincent.andrearczyk@hevs.ch
– henning.mueller@hevs.ch