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Proceedings Paper

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

The overall lower survival rate of patients with rare cancers can be explained, among other factors, by the limitations resulting from the scarce available information about them. Large biomedical data repositories, such as PubMed Central Open Access (PMC-OA), have been made freely available to the scientific community and could be exploited to advance the clinical assessment of these diseases. A multimodal approach using visual deep learning and natural language processing methods was developed to mine out 15,028 light microscopy human rare cancer images. The resulting data set is expected to foster the development of novel clinical research in this field and help researchers to build resources for machine learning.

Paper Details

Date Published: 2 March 2020
PDF: 11 pages
Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 113180A (2 March 2020); doi: 10.1117/12.2549565
Show Author Affiliations
Anjani Dhrangadhariya, HES-SO Valais-Wallis (Switzerland)
Oscar Jimenez-del-Toro M.D., HES-SO Valais-Wallis (Switzerland)
Vincent Andrearczyk, HES-SO Valais-Wallis (Switzerland)
Manfredo Atzori, HES-SO Valais-Wallis (Switzerland)
Henning Müller, HES-SO Valais-Wallis (Switzerland)
Univ. of Geneva (Switzerland)


Published in SPIE Proceedings Vol. 11318:
Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Thomas M. Deserno, Editor(s)

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