Exploiting biomedical literature to mine out a large multimodal dataset of rare cancers
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This document summarizes a study that mined biomedical literature to create a large multimodal dataset of rare cancer studies. The researchers harvested over 15,000 images and corresponding journal articles related to rare cancers from public literature databases. They used both visual and textual classification approaches to identify images of humans, neoplastic tissues, and rare cancers. The textual approach using TF-IDF and SVMs outperformed visual CNN classifiers for all tasks. This created the first dataset aimed at automatically extracting rare cancer images to help address challenges in researching these less prevalent cancers.
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Exploiting biomedical literature to mine out a large multimodal dataset of rare cancers
- 1. Exploiting biomedical literature to mine out a large multimodal dataset of rare cancer studies Anjani K. Dhrangadhariya et al. MedGIFT group University of Applied Sciences Western Switzerland (HES-SO) Project supported by European Union Horizon 2020 grant agreement 825292 SPIE Medical Imaging 2020, 16.02.2020
- 2. Motivation > Rare cancers = 15 out of 100,000 / year > Account for 25% cancer-related deaths > Lower prevalence = fewer patients > Less tumor samples for research > Lack of robust clinical models Puca, Loredana, et al. "Patient derived organoids to model rare prostate cancer phenotypes." Nature communications 9.1 (2018): 1-10. 2
- 3. Data resource • Challenges 1) Private datasets 2) Limited size 3) Single center / scanner 4) Small variability 5) Some contain only images / only text 6) No or small subsets of manual annotations 7) Difficult to compare results 3
- 4. Medline/PubMed PubMed / Medline PubMed Central PubMed Central Open- Access (PMC-OA) https://www.nlm.nih.gov/bsd/difference.html 30 million articles ~ 80 million images 5.9 million full texts 2.09 million full texts 6.73 million images 4 Rare cancer image harvesting through automated knowledge aggregation and data mining approaches? 2019
- 5. Individual record Medical Subject Headings (MeSH) Title + Abstract Images 1 2 3 5 ✓ ✓ ✓ ✓
- 6. Medical Subject Headings (MeSH) • Hierarchically organized Controlled Vocabulary • Cataloguing biomedical information • 16 thematic categories • A = Anatomy • B = Organism… • Each term has a unique MeSH Identifier MeSH term MeSH code Lipscomb, Carolyn E. "Medical subject headings (MeSH)." Bulletin of the Medical Library Association 88.3 (2000): 265. 6
- 7. MeSH as annotation • Manually annotated by National library of Medicine (NLM) staff • For e.g., All the studies about benign cancer are indexed under MeSH annotation “Neoplasm” • Groundtruth annotation • Not all PMC / PMCOA have annotations 7
- 8. Visual classification • ImageCLEF medical image annotation challenge (since 2013) • Small subset of annotated PMC-OA > train CNNs • Classify into 31 modalities - PET, light microscopy, CT, etc. • State of the art: Superficial modality classification 8 Deep Multimodal Classification of Image Types in Biomedical Journal Figures”, Andrearczyk and Müller, CLEF 2018 2000 Annotated PMC-OA 90% accuracy
- 9. Pipeline 99 Getting DLMI images Getting “human” images Getting “neoplastic” images Getting “rare cancer” images PMC-OA all images 1 2 3 4 5 DLMI Diagnostic Light Microscopy Images
- 10. 10 Pipeline Getting DLMI images Getting “human” images Getting “neoplastic” images Getting “rare cancer” images PMC-OA all images 1 2 3 4 5 Title + Abstract MeSH MeSH vs Visual Textual DLMI Diagnostic Light Microscopy Images
- 11. Visual approach: CNNs 11 MeSH_1 MeSH_0 Model training and evaluation • VGG19 • ImageNet weights • With and without image augmentation
- 12. Visual approach: CNNs 12 MeSH_1 MeSH_0 No MeSH MeSH_1MeSH_0 Model training and evaluation • VGG19 • ImageNet weights • With and without image augmentation
- 13. Title + Abstract Title + Abstract Textual approach Title + Abstract Model training & evaluation Best performing model 13 MeSH_0 MeSH_1 Title + Abstract MeSH_0 MeSH_1 Title + Abstract No MeSH
- 14. 14 Pipeline Getting DLMI images Getting “human” images Getting “neoplastic” images Getting “rare cancer” images PMC-OA all images 1 2 3 4 5 Title + Abstract MeSH MeSH vs
- 15. - 0.5467 0.1111 0.5789 - 0.3789 - 0.4999 0.6687 - 0.1167 0.9976 Getting “human” images Title + Abstract Title + Abstract Title + Abstract {MeSH} DLMI human Model training and evaluation 1. Logistic regression 2. Support Vector Machine 3. K-nearest neighbor 1. Tf-idf, 2. Word vectors, 3. paragraph vector Not human 20% 80% Training set Test set human Not human Title + Abstract Title + Abstract = = ⇔ B01.050.150.900.649.313.988.400.112.400.400 ∉ {MeSH} ⇔ B01.050.150.900.649.313.988.400.112.400.400 ∈ {MeSH} & other B01 codes ∉ {MeSH} 15
- 16. Getting “human” images Title + Abstract Title + Abstract human not human Best performing Model, hyper-params and vectors SVM, tf-idf bigrams No MeSH Title + Abstract DLMI Title + Abstract Title + Abstract Title + Abstract {MeSH} DLMI human Model training and evaluation 1. Logistic regression 2. Support Vector Machine 3. K-nearest neighbor 1. Tf-idf, 2. Word vectors, 3. paragraph vector not human 20% 80% Training set Test set - 0.5467 0.1111 0.5789 - 0.3789 - 0.4999 0.6687 - 0.1167 0.9976 16
- 17. 17 Pipeline Getting DLMI images Getting “human” images Getting “neoplastic” images Getting “rare cancer” images PMC-OA all images 1 2 3 4 5 Title + Abstract MeSH MeSH vs
- 18. 18 Getting “neoplastic” images neoplastic not neoplastic Title + Abstract Title + Abstract = = ⇔ C04 ∉ {MeSH} ⇔ C04 ∈ {MeSH} Title + Abstract Title + Abstract Title + Abstract {MeSH} DLMI Model training and evaluation 1. Logistic regression 2. Support Vector Machine 3. K-nearest neighbor 1. Tf-idf, 2. Word vectors, 3. paragraph vector 20% 80% Training set Test set human neoplastic not neoplastic - 0.5467 0.1111 0.5789 - 0.3789 - 0.4999 0.6687 - 0.1167 0.9976
- 19. Getting “non-neoplastic” images Title + Abstract Title + Abstract Title + Abstract {MeSH} DLMI Model training and evaluation 1. Logistic regression 2. Support Vector Machine 3. K-nearest neighbor 1. Tf-idf, 2. Word vectors, 3. paragraph vector 20% 80% Training set Test set human neoplastic not neoplastic Title + Abstract Title + Abstract Best performing Model, hyper-params and vectors SVM, tf-idf bigrams No MeSH Title + Abstract DLMI human neoplastic not neoplastic - 0.5467 0.1111 0.5789 - 0.3789 - 0.4999 0.6687 - 0.1167 0.9976 19
- 20. 20 Pipeline Getting DLMI images Getting “human” images Getting “neoplastic” images Getting “rare cancer” images PMC-OA all images 1 2 3 4 5 Title + Abstract MeSH MeSH vs
- 21. Getting “rare cancer” images • No MeSH terms for “rare” cancer class • Set of {rare cancer} terms by National Center for Advancing Translational Sciences (NCATS) https://rarediseases.info.nih.gov/diseases/diseases-by-category/1 21 Title + Abstract Title + Abstract DLMI humanNo MeSH {MeSH} DLMI neoplastic human neoplastic Title + Abstract rare cancer Title + Abstract rare cancer = ⇔ Title + Abstract ∩ {rare cancer} ≠ Ø Title + Abstract non-rare cancer
- 22. Visual: “rare cancer” 22 rare cancer Model training and evaluation • VGG19 • ImageNet weights • With and without image augmentation non-rare cancer
- 23. Visual: “rare cancer” 23 No label Model training and evaluation • VGG19 • ImageNet weights • With and without image augmentation rare cancer non-rare cancer rare cancer non-rare cancer
- 24. Results “human” vs. “non-human” classification Data type Classifier Feature Precision Recall F1-score Visual VGG19 With data augmentation 0.69 0.71 0.68 Textual SVM Tf-idf trigrams 0.89 0.90 0.90 24
- 25. Results “human” vs. “non-human” classification Data type Classifier Feature Precision Recall F1-score Visual VGG19 With data augmentation 0.69 0.71 0.68 Textual SVM Tf-idf trigrams 0.89 0.90 0.90 “neoplastic” vs. “non-neoplastic” classification Data type Classifier Feature Precision Recall F1-score Visual VGG19 With data augmentation 0.68 0.65 0.64 Textual SVM Tf-idf bigrams 0.99 0.99 0.99 25
- 26. Results “human” vs. “non-human” classification Data type Classifier Feature Precision Recall F1-score Visual VGG19 With data augmentation 0.69 0.71 0.68 Textual SVM Tf-idf trigrams 0.89 0.90 0.90 “neoplastic” vs. “non-neoplastic” classification Data type Classifier Feature Precision Recall F1-score Visual VGG19 With data augmentation 0.68 0.65 0.64 Textual SVM Tf-idf bigrams 0.99 0.99 0.99 “rare cancer” vs. “non-rare cancer” classification Data type Classifier Feature Precision Recall F1-score Visual VGG19 With data augmentation 0.62 0.77 0.69 26
- 27. Discussion: Textual vs. Visual 27 Textual approach Outperformed visual approach for all tasks Tf-idf n-grams with SVM performed the excellent for both tasks. Visual approach Correctly classify some “human” test instances with recall of 0.71 Worse performance for “neoplastic” identification “rare cancer” classification had a recall of 0.77
- 28. Conclusion • First study targeting automatic rare cancer image extraction • Used approach relies on visual deep learning and textual NLP • 15,028 light microscopy (DLMI), human, rare cancer images + corresponding journal articles Getting DLMI images Getting “human” images Getting “neoplastic” images Getting “rare cancer” images PMC-OA all data 28 1 2 3 4 5
- 29. Thank you for your attention 29 More information: http://medgift.hevs.ch Contact: anjani.dhrangadhariya@hevs.ch Follow us: https://twitter.com/MedGIFT_group
Notas del editor
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- How are these biomedical publications stored in Medline represented in PubMed? A PubMed record consists of Title and Abstract followed by Publication images as shown in thumbnails. And a list of Medical Subject Headings or MeSH annotations that are like keywords or annotations describing something about the publication. All these text, images and MeSH terms are stringed together by the unique PubMed Identifier or PMID. You can also notice a PMCID or unique pubmed central identifier that links to the full-text of the publication. All these components, the images, text and the MeSH terms have thus 1 to 1 association with each other.
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- PubMed records are manually annotated with MeSH terms by staff at NLM. What is the significance of attaching MeSH terms to a PubMed record? MeSH annotation enforces uniformity and consistency across the terminology in a way that all articles about benign cancer are indexed under MeSH term “Neoplasm”, all the articles or studies involving patients are annotated under MeSH term “Humans” So MeSH terms could be considered as gold standard annotations or groundtruth annotations for a publication. Not all publications in PubMed have these manually attached MeSH terms.
- Have this PMC-OA images been used elsewhere for image analysis? Yes, an annotated subset of PMC-OA has already been used in ImageCLEF medical image annotation challenge which is a public challenge that has been taking place since 2013. This small annotated subset of 2000 images was used to train CNNs for image classification into 31 image modality classes… Including PET, CT images, light microscopy images, et cetera. This classification approach achieved an overall 90% accuracy for modality classification. However, this approach only goes till superficial modality classification task. What about going beyond this generic modality classification into more specialized image sets?
- So what we did for navigating towards rare cancer sets was this: Take all the PMC-OA images and classify them using ImageCLEF setup into 31 modality types. Retain all the images classified as DLMI or diagnostic light microscopy images. We focus only upon DLMI images because they are fundamental to rare cancer diagnostics. All the retained DLMI images are linked to their respective title, abstract and MeSH annotations if available. With this multimodal annotated dataset in hand, we propose an approach for sequential curation of article abstracts and images using MeSH terms to eventually mine-out a large multimodal set of rare cancer images and full-texts.
- This involves three subsequent binary classification tasks where we first filter “human” from “non-human” set, followed by separating “neoplastic” from “non-neoplastic” set and finally separating “rare cancer“ from the “non-rare cancer“. It has to be noticed that at each binary classification step we compare visual vs. textual approach separately and use MeSH terms as the groundtruth labels for the datasets.
- For the visual classification tasks, images with two different MeSH classes were used to and evaluate VGG19 model using pretrained trained ImageNet weights and fine-tuned with and without image augmentation Data augmentation: image mirroring and cropping. Why do we use VGG?
- This fined-tuned models were then used to classify unlabeled images into their respective classes.
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- Lets get back to the pipeline for further curating the previously retrieved DLMI dataset. «human» records were first filtered out from «non-human records» in following way.
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- Best performing model setup was used to classify the un-annotated DLMI records into “human” and “non-human”.
- Then «neoplastic» or tumor-related records were separated from «non-neoplastic» records in similar manner.
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- Best performing model setup was used to classify the un-annotated records into “neoplasm” and “non-neoplasm”. This was about the annotated text dataset. Similarly, the annotated image dataset classified using VGG19 setup.
- Finally, we chaff out rare cancer dataset from the non-rare cancer dataset.
- Unfortunately, there are no MeSH terms pertaining to “rare cancer”, so we used a pre-defined set of rare cancer terms available from NCATS. All the records recognized as “neoplasm” were retained and filtered out as “rare cancer” only if rare cancer term from NCATS set was present in the title and the abstract.
- After getting «rare cancer» and the «non-rare cancer» labels for images from the previous text classification, we used them to train and evaluate a VGG19 model for this binary classification task.
- After getting «rare cancer» and the «non-rare cancer» labels for images from the previous text classification, we used them to train and evaluate a VGG19 model for this binary classification task.
- For the «human» classification task, textual approach performed far better than visual approach. However, a recall of 0.71 hints that the visual classification model does learn something about retaining human images.
- For the neoplasm classification task too, textual performed better than visual. Visual approach did not have good results for this task.
- For the final task, a recall of 0.77 does hint that VGG19 model did learn something by better retaining the «rare cancer» images, but it has much room for improvement.
- Classification: Individual images ≠ full-texts