Pancreatic cancer is a lethal form of cancer that significantly contributes to cancer-related deaths worldwide. Early detection is essential to improve patient prognosis and survival rates. Despite advances in medical imaging techniques, pancreatic cancer remains a challenging disease to detect. Endoscopic ultrasound (EUS) is the most effective diagnostic tool for detecting pancreatic cancer. However, it requires expert interpretation of complex ultrasound images to complete a reliable patient scan. To obtain complete imaging of the pancreas, practitioners must learn to guide the endoscope into multiple "EUS stations" (anatomical locations), which provide different views of the pancreas. This is a difficult skill to learn, involving over 225 proctored procedures with the support of an experienced doctor. We build an AI-assisted tool that utilizes deep learning techniques to identify these stations of the stomach in real time during EUS procedures. This computer-assisted diagnostic (CAD) will help train doctors more efficiently. Historically, the challenge faced in developing such a tool has been the amount of retrospective labeling required by trained clinicians. To solve this, we developed an open-source user-friendly labeling web app that streamlines the process of annotating stations during the EUS procedure with minimal effort from the clinicians. Our research shows that employing only 43 procedures with no hyperparameter fine-tuning obtained a balanced accuracy of 90%, comparable to the current state of the art. In addition, we employ Grad-CAM, a visualization technology that provides clinicians with interpretable and explainable visualizations.

翻译：胰腺癌是一种致死性癌症，在全球癌症相关死亡中占显著比例。早期检测对于改善患者预后和生存率至关重要。尽管医学影像技术不断进步，胰腺癌仍是一种难以检测的疾病。内镜超声（EUS）是目前诊断胰腺癌最有效的工具。然而，它需要专家解读复杂的超声图像以完成可靠的患者扫描。为获得胰腺的完整成像，操作者必须学习将内镜引导至多个"EUS站点"（解剖位置），这些站点提供胰腺的不同视图。这是一项难以掌握的技能，需要在资深医生指导下完成超过225次有监督操作。我们构建了一个基于深度学习的AI辅助工具，可在EUS操作过程中实时识别胃部这些站点。这种计算机辅助诊断（CAD）将有助于更高效地培训医生。以往开发此类工具的挑战在于需要经过培训的临床医生进行大量回顾性标注。为解决这一问题，我们开发了开源、用户友好的标注网页应用，可简化EUS操作中站点注释流程，最大限度减少临床医生的工作量。研究表明，仅使用43次操作且无需超参数调优即可获得90%的平衡准确率，与当前最优性能相当。此外，我们采用可视化技术Grad-CAM，为临床医生提供可解释、可理解的视觉呈现。