Large-scale Gastric Cancer Screening and Localization Using Multi-task Deep Neural Network

TL;DR

This paper presents a multi-task deep neural network framework for large-scale gastric cancer screening and lesion localization in whole-slide images, achieving high accuracy and robustness across multiple medical centers.

Contribution

The study introduces a novel multi-network framework trained on a large annotated dataset for simultaneous screening and lesion segmentation in gastric cancer detection.

Findings

97.05% sensitivity in screening

92.72% specificity in screening

Dice coefficient of 0.8331 in segmentation

Abstract

Gastric cancer is one of the most common cancers, which ranks third among the leading causes of cancer death. Biopsy of gastric mucosa is a standard procedure in gastric cancer screening test. However, manual pathological inspection is labor-intensive and time-consuming. Besides, it is challenging for an automated algorithm to locate the small lesion regions in the gigapixel whole-slide image and make the decision correctly.To tackle these issues, we collected large-scale whole-slide image dataset with detailed lesion region annotation and designed a whole-slide image analyzing framework consisting of 3 networks which could not only determine the screening result but also present the suspicious areas to the pathologist for reference. Experiments demonstrated that our proposed framework achieves sensitivity of 97.05% and specificity of 92.72% in screening task and Dice coefficient of 0.8331 in segmentation task. Furthermore, we tested our best model in real-world scenario on 10,315 whole-slide images collected from 4 medical centers.