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.

翻译：胃肠道内窥镜检查是一种利用配备摄像头及其他器械的柔性导管检查消化道的医疗程序。这种微创技术可用于诊断和管理多种胃肠道疾病，包括炎症性肠病、消化道出血和结肠癌。上消化道病变的早期检测与识别，以及具有癌变风险的恶性息肉辨别，是内窥镜诊疗应用的关键环节。因此，提升胃肠道疾病的检出率可显著改善患者预后——通过增加及时医疗干预的可能性，从而延长患者寿命并改善整体健康结局。本文提出一种新型基于Transformer的深度神经网络，能够同时执行多项任务，从而实现对上消化道病变和结肠息肉的高精度识别。该方法通过设计独特的全局上下文感知模块，结合强大的MiT主干网络与特征对齐模块，增强网络的表征能力。这一创新设计显著提升了多种内窥镜诊断任务的性能表现。大量实验证明，本方法相较于其他前沿技术具有优越性能。