Deep learning models for gastric signet ring cell carcinoma classification in whole slide images

TL;DR

This study develops deep learning models to accurately classify gastric signet ring cell carcinoma in whole slide images, achieving high ROC AUC scores and aiding pathologists in diagnosis.

Contribution

It introduces a comprehensive approach using transfer, fully-supervised, and weakly-supervised learning for SRCC detection in WSIs, setting a new performance benchmark.

Findings

Best model achieved ROC AUC of at least 0.99 on all test sets.

Models trained on 1,765 WSIs generalize well across different datasets.

Deep learning can effectively assist in SRCC diagnosis in pathology.

Abstract

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.