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Deep learning models to classify epithelial tumours of stomach and colon for supporting routine histopathological diagnosis

Integrating artificial intelligence (AI) within the computational pathology workflow would be of high benefit for easing the ever increasing workloads on pathologists, especially in regions that have shortages in access to pathological diagnosis services

How it started? According to global cancer statistics, stomach and colon cancers are amongst the most common leading causes of cancer deaths in the world, with stomach cancer ranking fourth in men and seventh in women, and colon cancer ranking third in men and second in women. Histopathological classification of gastric and colonic epithelial tumours is one of the routine pathological diagnosis tasks for pathologists. Computational pathology techniques based on Artificial intelligence (AI) would be of high benefit in easing the ever increasing workloads on pathologists, especially in regions that have shortages in access to pathological diagnosis services.

Our paper was published in “Scientific Reports” by Nature Research

We are delighted to share that our manuscript entitled “Deep learning models for histopathological classification of gastric and colonic epithelial tumours” by Koji Arihiro M.D., Ph.D. and Kei Kato from Hiroshima University along with Medmain members Osamu Iizuka, Fahdi Kanavati, Michael Rambeau, Masayuki Tsuneki, was published in authoritative Scientific Reports, a journal of the Nature Research on Jan 30th.