本文由小咖机器人翻译整理

期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-024-02856-4

摘要内容如下：

数字病理学的加速采用和深度学习的进步使得能够为各种疾病和患者队列的各种病理学任务开发强大的模型。然而，由于医学领域中的标签稀缺，模型训练通常是困难的，并且模型的使用受到其所训练的特定任务和疾病的限制。此外，组织病理学中的大多数模型仅利用图像数据，这与人类如何相互教导和推理组织病理学实体形成鲜明对比。我们介绍了组织病理学字幕对比学习（CONCH），这是一种视觉语言基础模型，使用组织病理学图像、生物医学文本的不同来源开发，值得注意的是，通过任务不可知的预训练，超过117万个图像字幕对。在一套14个不同的基准上进行评估，海螺可以转移到涉及组织病理学图像和/或文本的广泛的下游任务，在组织学图像分类、分割、字幕、文本到图像和图像到文本检索方面实现最先进的性能。与组织病理学的并行视觉语言预训练系统相比，Conch代表了一次重大飞跃，有可能直接促进一系列基于机器学习的工作流程，只需最少或无需进一步监督微调。

英文原文如下：

Abstracts

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model's usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text and, notably, over 1.17 million image-caption pairs through task-agnostic pretraining. Evaluated on a suite of 14 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving histopathology images and/or text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, and text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning.

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