What You Should Know:

您应该知道：

– Researchers at Mass General Brigham have developed two powerful AI models, UNI and CONCH, that represent a significant leap forward in computational pathology (CPath).

–马萨诸塞州布里格姆将军的研究人员开发了两种强大的人工智能模型，UNI和CONCH，它们代表了计算病理学（CPath）的重大飞跃。

– Published today in Nature Medicine, the two new foundation models, trained on massive datasets, hold immense potential for improving diagnoses, predicting patient outcomes, and even identifying rare diseases.

–这两个新的基金会模型今天发表在《自然医学》杂志上，经过大量数据集的训练，在改善诊断、预测患者预后甚至识别罕见疾病方面具有巨大潜力。

What are Foundation Models?

什么是基础模型？

Foundation models are advanced AI systems trained on vast amounts of data. This training allows them to learn complex patterns and relationships within the data. In CPath, foundation models are trained on pathology images, often accompanied by corresponding medical text descriptions. This empowers them to analyze tissue samples, identify abnormalities, and glean insights from associated reports..

基础模型是在大量数据上训练的高级人工智能系统。这种培训使他们能够学习数据中的复杂模式和关系。在CPath中，基础模型在病理图像上进行训练，通常伴随着相应的医学文本描述。这使他们能够分析组织样本，识别异常情况，并从相关报告中收集见解。。

Understanding UNI and CONCH Foundational Models

了解UNI和海螺基础模型

UNI: This model focuses on understanding pathology images, excelling at tasks like disease detection and whole slide image analysis. Trained on over 100 million tissue samples, UNI leverages transfer learning to apply its knowledge to various clinical scenarios. It outperformed existing models in 34 tasks, showcasing its adaptability and potential as a versatile CPath tool.CONCH: This unique model integrates image and text data, allowing pathologists to interact with it using medical terminology.

UNI：该模型专注于理解病理图像，擅长疾病检测和全幻灯片图像分析等任务。UNI接受了超过1亿个组织样本的培训，利用转移学习将其知识应用于各种临床情况。它在34个任务中优于现有模型，展示了其作为多功能CPath工具的适应性和潜力。海螺：这种独特的模型集成了图像和文本数据，允许病理学家使用医学术语与之交互。

This enables CONCH to excel at tasks like identifying rare diseases, segmenting tumors, and understanding complex whole slide images. In 14 clinical evaluations, CONCH surpassed standard models, demonstrating its effectiveness and versatility..

这使海螺能够在识别罕见疾病，分割肿瘤以及理解复杂的整张幻灯片图像等任务中表现出色。在14项临床评估中，海螺超过了标准模型，证明了其有效性和多功能性。。

UNI and CONCH Foundational Model Benefits and Future Implications

UNI和海螺基础模型的好处和未来的影响

These foundation models offer several advantages:

这些基础模型有几个优点：

Improved Diagnostic Accuracy: UNI and CONCH can potentially lead to more accurate diagnoses, aiding pathologists in complex cases.Enhanced Prognostic Insights: The models may provide valuable insights into disease progression and patient outcomes.Rare Disease Identification: CONCH’s ability to analyze text descriptions might prove crucial in identifying rare diseases with limited visual data..

提高诊断准确性：UNI和海螺可能会导致更准确的诊断，有助于复杂病例的病理学家。增强预后见解：这些模型可能为疾病进展和患者预后提供有价值的见解。罕见疾病识别：海螺分析文本描述的能力可能对识别视觉数据有限的罕见疾病至关重要。。

Availability

可用性

The research team is making the code publicly available, encouraging further development and application in tackling real-world clinical problems. This marks a significant step towards a new era of AI-powered CPath, with the potential to revolutionize medical diagnosis and patient care.

研究团队正在公开该代码，鼓励进一步开发和应用于解决现实世界的临床问题。这标志着迈向人工智能驱动的CPath新时代的重要一步，有可能彻底改变医学诊断和患者护理。

“Foundation models represent a new paradigm in medical artificial intelligence,” said corresponding author Faisal Mahmood, PhD, of the Division of Computational Pathology in the Department of Pathology at Mass General Brigham. “These models are AI systems that can be adapted to many downstream, clinically relevant tasks.

“基础模型代表了医学人工智能的一种新范式，”通讯作者费萨尔·马哈茂德博士说，他是马萨诸塞州布里格姆将军病理学系计算病理学系的一名博士。“这些模型是人工智能系统，可以适应许多下游的临床相关任务。

We hope that the proof-of-concept presented in these studies will set the stage for such self-supervised models to be trained on larger and more diverse datasets.”.

我们希望这些研究中提出的概念验证将为这种自我监督模型在更大和更多样化的数据集上进行训练奠定基础。”。