Deep learning models for gastric signet ring cell carcinoma classification in whole slide images
This work provides a computational pathology tool to assist pathologists in the difficult detection of gastric SRCC, which has poor prognosis if detected late.
The paper developed deep learning models to classify gastric signet ring cell carcinoma (SRCC) in Whole Slide Images (WSIs). The best model achieved an AUC of at least 0.99 across four test sets, establishing a new baseline for SRCC WSI classification.
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 four different test sets of about 500 images each. The best model achieved a Receiver Operator Curve (ROC) area under the curve (AUC) of at least 0.99 on all four test sets, setting a top baseline performance for SRCC WSI classification.