{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Schaumberg AJ"],"funding":["NCI NIH HHS","U.S. Department of Health & Human Services | National Institutes of Health","NIGMS NIH HHS"],"pagination":["2169-2185"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7581495"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["33(11)"],"pubmed_abstract":["Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease st"],"journal":["Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc"],"pubmed_title":["Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media."],"pmcid":["PMC7581495"],"funding_grant_id":["F31CA214029","P30 CA008748","F31 CA214029","T32 GM083937"],"pubmed_authors":["Choudhury SJ","Stayerman C","Sotillo Sanchez R","Morini A","Pastrian LG","Folaranmi OO","Sirintrapun SJ","Ali RH","Sener BD","Xu B","Rosado-Orozco K","Jones KA","Yang H","Pritt BS","Prieto Pozuelo M","Yip S","Gardner JM","Ho K","Juarez-Nicanor WC","Suleiman DE","Schaumberg AJ","Rosen Y","Miguel C","Zahra N","Annavarapu SR","Gross J","Mukhopadhyay S","Rusu C","Aly M","Fuchs TJ"],"additional_accession":[]},"is_claimable":false,"name":"Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media.","description":"Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease st","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Nov","modification":"2025-04-18T14:43:39.705Z","creation":"2020-11-08T09:46:35Z"},"accession":"S-EPMC7581495","cross_references":{"pubmed":["32467650"],"doi":["10.1038/s41379-020-0540-1"]}}