Successful Development of Deep Learning Models to Classify Gastric Signet Ring Cell Carcinoma in Histopathological Whole Slide Images - Published in Technology in Cancer Research and Treatment -

Medmain Inc., a provider of “PidPort” pathological diagnosis support solutions, has succeeded in developing deep learning models to classify gastric signet ring cell carcinoma in whole slide images of endoscopic biopsy specimens through a multi-institutional joint research.

We would also like to announce that a paper on this development has been submitted to Technology in Cancer Research and Treatment, and was published on June 30, 2021.

DOI: https://journals.sagepub.com/doi/10.1177/15330338211027901

Signet ring cell carcinoma (SRCC) of the stomach is a rare type of cancer with a slowly rising incidence. It tends to be more difficult to detect by pathologists, mainly due to its cellular morphology and diffuse invasion manner, and it has poor prognosis when detected at an advanced stage. Computational pathology tools that can assist pathologists in detecting SRCC would be of a massive benefit. In this paper, we trained deep learning models using transfer learning, fully-supervised learning, and weakly-supervised learning to predict SRCC in Whole Slide Images (WSIs) using a training set of 1,765 WSIs. We evaluated the models on two different test sets (n = 999, n = 455). The best model achieved a ROC-AUC of at least 0.99 on all two test sets, setting a top baseline performance for SRCC WSI classification.