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：胰腺癌是一种致命的癌症，是全球癌症相关死亡的重要原因。早期检测对于改善患者预后和生存率至关重要。尽管医学成像技术取得了进步，胰腺癌仍然是一种难以检测的疾病。内镜超声（EUS）是检测胰腺癌最有效的诊断工具。然而，它需要专家解读复杂的超声图像才能完成可靠的患者扫描。为了获得胰腺的完整成像，从业者必须学会将内镜引导至多个“EUS站”（解剖位置），这些位置提供胰腺的不同视角。这是一项难以掌握的技能，需要在经验丰富的医生的支持下进行超过225次受监督的操作。我们构建了一个AI辅助工具，利用深度学习技术在内镜超声过程中实时识别胃部的这些站。这种计算机辅助诊断（CAD）将有助于更高效地培训医生。历史上，开发此类工具面临的挑战是训练有素的临床医生需要进行的回顾性标注工作量。为了解决这个问题，我们开发了一个开源、用户友好的标注网络应用程序，该应用程序在EUS过程中以最少的临床医生努力简化了站点注释流程。我们的研究表明，仅使用43次操作且无需超参数调整即可获得90%的平衡准确率，与当前最先进水平相当。此外，我们采用了Grad-CAM，一种可视化技术，为临床医生提供可解释和可解释的可视化结果。