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Histopathological examination is very important for diseases diagnosis and treatment. With the development of artificial intelligence, more and more pathological databases have been reported for histopathological diagnosis because database is quite crucial for the validation and testing of feature extraction, statistical analysis and deep learning algorithms. However, most of these databases are either gray images or RGB color images of tissue sections contain limited information of samples which limited the performance of most current deep learning algorithms. There are few publicly available pathological databases that include more than two modalities for the same subject. This paper introduces a database for both microscopy hyperspectral and color images of cholangiocarcinoma, including 880 scenes from 174 individuals, among which 689 scenes are samples with part of cancer areas, 49 scenes full of cancer areas, and 142 scenes without cancer areas. In addition, all cancer areas have been precisely labeled by experienced pathologists. The contributions of this work: a) A comprehensive and up-to-date review on pathological imaging systems and databases; b) Detailed description of the proposed the multidimensional Choledoch Database and login method; c) The multidimensional Choledoch Database has been published and can be downloaded after registration and made an entry on the website.

A Multidimensional Choledoch Database and Benchmarks for Cholangiocarcinoma Diagnosis