UGCANet: A Unified Global Context-Aware Transformer-based Network with
  Feature Alignment for Endoscopic Image Analysis
Pham Vu Hung Nguyen Duy Manh Nguyen Thi Oanh Nguyen Thi Thuy Dinh Viet Sang
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.