<HashMap><database>bioimages</database><scores/><additional><omics_type>Unknown</omics_type><submitter/><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-BIAD1714</full_dataset_link><repository>bioimages</repository><figure_sub>Specimen</figure_sub><figure_sub>Funding</figure_sub><figure_sub>Study Component</figure_sub><figure_sub>organisation</figure_sub><figure_sub>Biosample</figure_sub><figure_sub>Associations</figure_sub><figure_sub>Image acquisition</figure_sub><pubmed_authors>Julia Neumann</pubmed_authors><pubmed_authors>Antonia Gocke</pubmed_authors><pubmed_authors>Yannis Schumann</pubmed_authors></additional><is_claimable>false</is_claimable><name>COMET (Computational Pathology for Molecular Ependymoma Typing)</name><description>COMET works towards the deployment of advanced, interpretable artificial intelligence methods in routine diagnostics to facilitate rapid and accurate molecular diagnosis of ependymomas and other brain tumors based on histological image data to improve patient outcome. </description><dates><release>2025-03-21T00:00:00Z</release><modification>2025-03-17T13:01:07.426Z</modification><creation>2025-03-17T13:01:07.426Z</creation></dates><accession>S-BIAD1714</accession><cross_references/></HashMap>