Exploiting biomedical literature to mine out a large multimodal dataset of rare cancer studies

Dhrangadhariya, Anjani; Jiménez–del–Toro, Oscar; Andrearczyk, Vincent; Atzori, Manfredo; Müller, Henning

doi:10.1117/12.2549565

Cited by 3 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For images detected as histopathology to be useful for training clinical decision support systems, further steps are required [12]. Much of the cancer research is on animals so, when learning human tissue classification, it is important to filter out animal tissue samples even from the same organ.…”

Section: Combining Text and Images For Data Analysismentioning

confidence: 99%

“…With more than 2 million articles in total, an average of 3.5 figures per article including 1.5 compound figures of 4 subfigures each, it sums up to approximately seven million figures, including 3 million compound figures with 12 million subfigures for a total of >16 million figures available in 2018 if separated correctly. With an expected increase of nearly 3 million figures in 2019 and more in the following years, it promises in the near future very large amounts of training data in various applications, modalities and particularly rare cases that are strongly oversampled in the literature compared to clinical archives [12]. Difficulties related to this type of data, that will be developed in more details in Section 3, include the heterogeneity and non-guaranteed quality of the images, the presence of compound images and the automation of ground truth labels extraction from the text.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Studying Public Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction

Müller

Andrearczyk

Toro

et al. 2019

MultiMedia Modeling

Self Cite

View full text Add to dashboard Cite

Medical imaging research has long suffered problems getting access to large collections of images due to privacy constraints and to high costs that annotating images by physicians causes. With public scientific challenges and funding agencies fostering data sharing, repositories, particularly on cancer research in the US, are becoming available. Still, data and annotations are most often available on narrow domains and specific tasks. The medical literature (particularly articles contained in MedLine) has been used for research for many years as it contains a large amount of medical knowledge. Most analyses have focused on text, for example creating semi-automated systematic reviews, aggregating content on specific genes and their functions, or allowing for information retrieval to access specific content. The amount of research on images from the medical literature has been more limited, as MedLine abstracts are available publicly but no images are included. With PubMed Central, all the biomedical open access literature has become accessible for analysis, with images and text in structured format. This makes the use of such data easier than extracting it from PDF. This article reviews existing work on analyzing images from the biomedical literature and develops ideas on how such images can become useful and usable for a variety of tasks, including finding visual evidence for rare or unusual cases. These resources offer possibilities to train machine learning tools, increasing the diversity of available data and thus possibly the robustness of the classifiers. Examples with histopathology data available on Twitter already show promising possibilities. This article add links to other sources that are accessible, for example via the ImageCLEF challenges.

show abstract

Section: Combining Text and Images For Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Studying Public Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction

Müller

Andrearczyk

Toro

et al. 2019

MultiMedia Modeling

Self Cite

View full text Add to dashboard Cite

show abstract

“…Biomedical datasets are increasingly made publicly available leading to thousands of potentially available multi-modal data, e.g. from challenges [5][6][7] , open-access databases [8][9][10][11][12] , and scientific literature 13 . Some examples of online databases are UniProt 14 which aims to provide comprehensive and high-quality resources on protein sequences and functional information and the Kyoto Encyclopedia of Genes and Genomes (KEGG), a professional knowledge base for the biological interpretation of large-scale molecular datasets, such as genomic and metagenomic sequences 15 .Semantic-based approaches represent an extraordinary and increasingly exploited opportunity for biomedical sciences because they allow to create a unique, multilingual representation of medical concepts.While in Linguistics Semantics refers to the meaning of words, phrases, or sentences, in Computer Science Semantics refers to the study of properties, categories, and relationships among concepts of a specific area 16 .…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Semantic wikis as flexible database interfaces for biomedical applications

Falda

Atzori

Corbetta

2023

Sci Rep

View full text Add to dashboard Cite

Several challenges prevent extracting knowledge from biomedical resources, including data heterogeneity and the difficulty to obtain and collaborate on data and annotations by medical doctors. Therefore, flexibility in their representation and interconnection is required; it is also essential to be able to interact easily with such data. In recent years, semantic tools have been developed: semantic wikis are collections of wiki pages that can be annotated with properties and so combine flexibility and expressiveness, two desirable aspects when modeling databases, especially in the dynamic biomedical domain. However, semantics and collaborative analysis of biomedical data is still an unsolved challenge. The aim of this work is to create a tool for easing the design and the setup of semantic databases and to give the possibility to enrich them with biostatistical applications. As a side effect, this will also make them reproducible, fostering their application by other research groups. A command-line software has been developed for creating all structures required by Semantic MediaWiki. Besides, a way to expose statistical analyses as R Shiny applications in the interface is provided, along with a facility to export Prolog predicates for reasoning with external tools. The developed software allowed to create a set of biomedical databases for the Neuroscience Department of the University of Padova in a more automated way. They can be extended with additional qualitative and statistical analyses of data, including for instance regressions, geographical distribution of diseases, and clustering. The software is released as open source-code and published under the GPL-3 license at https://github.com/mfalda/tsv2swm.

show abstract

Classification of Noisy Free-Text Prostate Cancer Pathology Reports Using Natural Language Processing

Dhrangadhariya

Otálora

Atzori

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

View full text Add to dashboard Cite

2 Gleason score 3+4 Gleason grade: a) Primary pattern 3/5 b) Secondary pattern 4/5 a) Total Gleason score 7/10 Swillens, J. E. M., et al. "Identification of barriers and facilitators in nationwide implementation of standardized structured reporting in pathology: a mixed method study." Snoek, Annefleure, et al. "The impact of standardized structured reporting of pathology reports for breast cancer in the Netherlands.

show abstract

Exploiting biomedical literature to mine out a large multimodal dataset of rare cancer studies

Cited by 3 publications

References 0 publications

Studying Public Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction

Studying Public Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction

Semantic wikis as flexible database interfaces for biomedical applications

Classification of Noisy Free-Text Prostate Cancer Pathology Reports Using Natural Language Processing

Contact Info

Product

Resources

About