UGCANet：一种用于内镜图像分析的、具备特征对齐的统一全局上下文感知 Transformer 网络
UGCANet: A Unified Global Context-Aware Transformer-based Network with Feature Alignment for Endoscopic Image Analysis
- Hanoi University of Science and Technology（河内科技大学）
- RMIT University（皇家墨尔本理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出 UGCANet，通过全局上下文感知模块、MiT 主干和特征对齐块构建多任务 Transformer 网络，以同时准确识别上胃肠道病变与结肠息肉，并在多项内镜诊断任务中优于现有方法。
AI中文摘要：
胃肠内镜是一种医疗操作，它使用配备摄像头和其他器械的柔性管来检查消化道。这种微创技术能够诊断和管理多种胃肠疾病，包括炎症性肠病、胃肠道出血和结肠癌。早期检测和识别上胃肠道病变，以及识别可能具有癌变风险的恶性息肉，是胃肠内镜诊断和治疗应用的关键组成部分。因此，提高胃肠疾病的检出率可以通过增加及时医疗干预的可能性，显著改善患者预后，从而可能延长患者寿命并改善整体健康结果。本文提出了一种新颖的基于 Transformer 的深度神经网络，旨在同时执行多项任务，从而准确识别上胃肠道病变和结肠息肉。我们的方法提出了一个独特的全局上下文感知模块，并利用强大的 MiT 主干网络以及特征对齐块来增强网络的表征能力。这一新颖设计使各类内镜诊断任务的性能得到显著提升。大量实验表明，与其他最先进方法相比，我们的方法具有优越性能。
英文摘要：
Gastrointestinal endoscopy is a medical procedure that utilizes a flexible tube equipped with a camera and other instruments to examine the digestive tract. This minimally invasive technique allows for diagnosing and managing various gastrointestinal conditions, including inflammatory bowel disease, gastrointestinal bleeding, and colon cancer. The early detection and identification of lesions in the upper gastrointestinal tract and the identification of malignant polyps that may pose a risk of cancer development are critical components of gastrointestinal endoscopy's diagnostic and therapeutic applications. Therefore, enhancing the detection rates of gastrointestinal disorders can significantly improve a patient's prognosis by increasing the likelihood of timely medical intervention, which may prolong the patient's lifespan and improve overall health outcomes. This paper presents a novel Transformer-based deep neural network designed to perform multiple tasks simultaneously, thereby enabling accurate identification of both upper gastrointestinal tract lesions and colon polyps. Our approach proposes a unique global context-aware module and leverages the powerful MiT backbone, along with a feature alignment block, to enhance the network's representation capability. This novel design leads to a significant improvement in performance across various endoscopic diagnosis tasks. Extensive experiments demonstrate the superior performance of our method compared to other state-of-the-art approaches.
