MAF-Net: Multi-Branch Anchor-Free Detector for Polyp Localization and Classification in Colonoscopy

Abstract

Colorectal polyps are abnormal tissues growing on the intima of the colon or rectum with a high risk of developing into colorectal cancer, the third leading cause of cancer death worldwide. The most common types of colorectal polyps include inflammatory, hyperplastic, and adenomatous polyps. Adenomatous polyps are the most dangerous type of polyp with the potential to become cancerous. Therefore, the prevention of colorectal cancer heavily depends on the identification and removal of adenomatous polyps. In this paper, we propose a novel framework to assist physicians to localize, identify, and remove adenomatous polyps in colonoscopy. The framework consists of an anchor-free polyp detection branch for detecting and localizing polyps and a classification branch for global feature extraction and pathology prediction. Furthermore, we propose a foreground attention module to generate local features from the foreground subnet in the detection branch, which are combined with the global feature in the classification branch to enhance the pathology prediction performance. We collect a dataset that contains 6,059 images with 6,827 object-level annotations. This dataset is the first large-scale polyp pathology dataset with both object segmentation annotations and pathology labels. Experiment results show that our proposed framework outperforms traditional CNN-based classifiers on polyp pathology classification and anchor-based detectors on polyp detection and localization.