OnSight病理科：一种实时、平台无关的计算病理科辅助工具，用于组织病理学
OnSight Pathology: A real-time platform-agnostic computational pathology companion for histopathology
AI总结：
OnSight病理科是一种实时、平台无关的计算病理科工具，通过本地运行的AI模型提供实时诊断支持，适用于多种病理学流程和场景。
摘要：
手术组织的显微检查仍然是疾病分类的基石，但依赖于主观解释和对高度专业专家的访问，这可能会损害准确性和临床护理。尽管人工智能（AI）的新兴突破为自动化组织学分析提供了希望，但越来越多的专有数字病理解决方案的出现阻碍了实际应用。为了解决这些挑战，我们介绍了OnSight病理科，这是一种平台无关的计算机视觉软件，利用连续的自定义屏幕截图，为用户在查看数字滑片图像时提供实时的AI推断。作为单个、自包含的可执行文件（https://onsightpathology.github.io/），OnSight病理科可以在消费级个人电脑上本地运行，而无需复杂的软件集成，从而在研究和临床流程中实现成本效益且安全的部署。在这里，我们通过超过2500张公开可用的完整滑片图像（不同滑片查看器）以及我们临床数字病理学设置中的病例，展示了OnSight病理科的实用性。该软件的鲁棒性在常规的组织病理学任务中得到突出显示，包括常见脑肿瘤类型的分类、有丝分裂检测以及免疫组化染色的定量。内置的多模态聊天助手提供可验证的图像描述，不带有严格的类别标签，以增加质量控制。最后，我们展示了其与实时显微镜摄像头流的兼容性，包括来自个人智能手机的流，为在更模拟、术中和远程病理学环境中部署提供了潜力。共同来看，我们展示了OnSight病理科如何在广泛的病理学流程中提供实时的AI推断，消除了AI工具在组织病理学中采用的关键障碍。
英文摘要：
The microscopic examination of surgical tissue remains a cornerstone of disease classification but relies on subjective interpretations and access to highly specialized experts, which can compromise accuracy and clinical care. While emerging breakthroughs in artificial intelligence (AI) offer promise for automated histological analysis, the growing number of proprietary digital pathology solutions has created barriers to real-world deployment. To address these challenges, we introduce OnSight Pathology, a platform-agnostic computer vision software that uses continuous custom screen captures to provide real-time AI inferences to users as they review digital slide images. Accessible as a single, self-contained executable file (https://onsightpathology.github.io/ ), OnSight Pathology operates locally on consumer-grade personal computers without complex software integration, enabling cost-effective and secure deployment in research and clinical workflows. Here we demonstrate the utility of OnSight Pathology using over 2,500 publicly available whole slide images across different slide viewers, as well as cases from our clinical digital pathology setup. The software's robustness is highlighted across routine histopathological tasks, including the classification of common brain tumor types, mitosis detection, and the quantification of immunohistochemical stains. A built-in multi-modal chat assistant provides verifiable descriptions of images, free of rigid class labels, for added quality control. Lastly, we show compatibility with live microscope camera feeds, including from personal smartphones, offering potential for deployment in more analog, inter-operative, and telepathology settings. Together, we highlight how OnSight Pathology can deliver real-time AI inferences across a broad range of pathology pipelines, removing key barriers to the adoption of AI tools in histopathology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
