Consisaug: A Consistency-based Augmentation for Polyp Detection in Endoscopy Image Analysis

Autor:	Zhou, Ziyu, Shen, Wenyuan, Liu, Chang
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Colorectal cancer (CRC), which frequently originates from initially benign polyps, remains a significant contributor to global cancer-related mortality. Early and accurate detection of these polyps via colonoscopy is crucial for CRC prevention. However, traditional colonoscopy methods depend heavily on the operator's experience, leading to suboptimal polyp detection rates. Besides, the public database are limited in polyp size and shape diversity. To enhance the available data for polyp detection, we introduce Consisaug, an innovative and effective methodology to augment data that leverages deep learning. We utilize the constraint that when the image is flipped the class label should be equal and the bonding boxes should be consistent. We implement our Consisaug on five public polyp datasets and at three backbones, and the results show the effectiveness of our method. Comment: MLMI 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.11355 Zobrazit plný text záznamu View this record from Arxiv