A gastric cancer recognition algorithm on gastric pathological sections based on multistage attention‐DenseNet. (18th March 2021)

• Record Type: Journal Article
• Authors: Liu, Bo; Zhao, Yelong; Yang, Bin; Zhao, Shuangtao; Gu, Rentao; Gahegan, Mark
• Is Part Of: Concurrency and computation. Volume 33:Number 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-03-18
• Subjects: computer‐assisted diagnosis -- deep learning -- gastric cancer -- gastric pathological sections; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6188
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: As an important method to diagnose gastric cancer, gastric pathological sections images (GPSI) are hard and time‐consuming to be recognized even by an experienced doctor. An efficient method was designed to detect gastric cancer in magnified (20×) GPSI using deep learning technology. A novel DenseNet architecture was applied, modified with a multistage attention module (MSA‐DenseNet). To develop this model focusing on gastric features, a two‐stage‐input attention module was adopted to select more semantic information of cancer. Moreover, the pretraining process was divided into two steps to improve the effect of the attention mechanism. After training, our method achieved a state‐of‐the‐art performance yielding 0.9947 F1 score and 0.9976 ROC AUC on a test dataset. In line with our expectation in clinical practice, a high recall (0.9929) was produced with high sensitivity to the positive samples. These results indicate that this new model performs better than current artificial detection approaches and its effectiveness is therefore validated in cancer pathological diagnoses.