ORCHID: A Comprehensive Oral Cancer Histology Image Database for Histopathological Analytics and Diagnostics

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 300,000 image patches, encapsulating various oral cancer and precancer categories, such as oral submucous fibrosis (OSMF) and oral squamous cell carcinoma (OSCC). Additionally, it also contains grade-level sub-classifications for OSCC, such as well-differentiated (WD), moderately-differentiated (MD), and poorly-differentiated (PD). Furthermore, the database seeks to bolster the creation and validation of innovative artificial intelligence-based rapid diagnostics for OSMF and OSCC, along with subtypes.