Predicting Differentiation Degree of Gastric Cancer Pathology Images Based on Mask Attention R-CNN

Gastric cancer is one of the highest mortality cancers in the world. At present, pathological diagnosis is the gold standard for gastric cancer diagnosis. Deep Learning technique has been widely used in pathological diagnosis using medical images. The differentiation of gastric cancer cells is one of the important contents in the pathology report. The degree of tumor differentiation reflects the degree of cell maturity and the degree of malignancy of the tumor. It is of great significance to carry out early treatment and prognostic diagnosis. However, there are few related researches on the calculation of gastric cancer cell differentiation degree in medical imaging, since it is difficult to accurately segment the ROI (region of interest) of gastric cancer cells. In this paper, we propose an improved Mask R-CNN framework that called Mask Attention R-CNN to predict the differentiation degree of gastric cancer cells. The framework contains a mask attention block (MAB) that can calculate the Intersection over Union (IoU) of the predicted gastric cancer cell mask and mask feature map to enhance the mask quality. We also add a scale jittering strategy which can learn detailed information from images. As far as we know, this is the first study to apply the instance segmentation framework to the task of predicting differentiation degree of gastric cancer cells. The mask branch effectively improves the accuracy of the instance mask of gastric cancer cells. Both the location and classification accuracies of gastric cancer cells are improved by our model. This can provide pathologists with auxiliary diagnosis opinions, which has certain clinical application value.