{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chaudhary N"],"funding":["DST | Science and Engineering Research Board (SERB)","Indian Council of Medical Research (ICMR)"],"pagination":["1050"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11436638"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(1)"],"pubmed_abstract":["Oral cancer is a global health challenge with a difficult histopathological diagnosis. The accurate histopathological interpretation of oral cancer tissue samples remains difficult. However, early diagnosis is very challenging due to a lack of experienced pathologists and inter- observer variability in diagnosis. The application of artificial intelligence (deep learning algorithms) for oral cancer histology images is very promising for rapid diagnosis. However, it requires a quality annotated dataset to build AI models. We present ORCHID (ORal Cancer Histology Image Database), a specialized database generated to advance research in AI-based histology image analytics of oral cancer and precancer. The ORCHID database is an extensive multicenter collection of high-resolution images captured a"],"journal":["Scientific data"],"pubmed_title":["High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma."],"pmcid":["PMC11436638"],"funding_grant_id":["GIA/2019/000274/PRCGIA (Ver-1)","3/1/2(1)/Oral/2021-NCD-II","CRG/2020/002294"],"pubmed_authors":["Mishra D","Chaudhary N","Ahmad T","Augustine J","Chandra A","Rai A","Rao AM","Faizan MI","Chauhan V","Chaurasia A"],"additional_accession":[]},"is_claimable":false,"name":"High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma.","description":"Oral cancer is a global health challenge with a difficult histopathological diagnosis. The accurate histopathological interpretation of oral cancer tissue samples remains difficult. However, early diagnosis is very challenging due to a lack of experienced pathologists and inter- observer variability in diagnosis. The application of artificial intelligence (deep learning algorithms) for oral cancer histology images is very promising for rapid diagnosis. However, it requires a quality annotated dataset to build AI models. We present ORCHID (ORal Cancer Histology Image Database), a specialized database generated to advance research in AI-based histology image analytics of oral cancer and precancer. The ORCHID database is an extensive multicenter collection of high-resolution images captured a","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Sep","modification":"2026-06-02T16:11:08.212Z","creation":"2025-04-06T22:30:42.948Z"},"accession":"S-EPMC11436638","cross_references":{"pubmed":["39333529"],"doi":["10.1038/s41597-024-03836-6"]}}