Developing generalist foundation model has recently attracted tremendous
attention among researchers in the field of AI for Medicine (AI4Medicine). A
pivotal insight in developing these models is their reliance on dataset
scaling, which emphasizes the requirements on developing open-source medical
image datasets that incorporate diverse supervision signals across various
imaging modalities. In this paper, we introduce RadGenome-Chest CT, a
comprehensive, large-scale, region-guided 3D chest CT interpretation dataset
based on CT-RATE. Specifically, we leverage the latest powerful universal
segmentation and large language models, to extend the original datasets (over
25,692 non-contrast 3D chest CT volume and reports from 20,000 patients) from
the following aspects: (i) organ-level segmentation masks covering 197
categories, which provide intermediate reasoning visual clues for
interpretation; (ii) 665 K multi-granularity grounded reports, where each
sentence of the report is linked to the corresponding anatomical region of CT
volume in the form of a segmentation mask; (iii) 1.3 M grounded VQA pairs,
where questions and answers are all linked with reference segmentation masks,
enabling models to associate visual evidence with textual explanations. All
grounded reports and VQA pairs in the validation set have gone through manual
verification to ensure dataset quality. We believe that RadGenome-Chest CT can
significantly advance the development of multimodal medical foundation models,
by training to generate texts based on given segmentation regions, which is
unattainable with previous relevant datasets. We will release all segmentation
masks, grounded reports, and VQA pairs to facilitate further research and
development in this field.

通过开源医学图像数据集、基于最新的通用分割和大型语言模型，我们介绍了 RadGenome-Chest CT，这是一个全面的、大规模的、基于 CT-RATE 的区域导向的 3D 胸部 CT 解读数据集，推动了多模态医学基础模型的发展。

RadGenome-Chest CT：用于胸部 CT 分析的基于视觉和语言的数据集

RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT  Analysis

Generalist foundation model has ushered in newfound capabilities in medical
domain. However, the contradiction between the growing demand for high-quality
annotated data with patient privacy continues to intensify. The utilization of
medical artificial intelligence generated content (Med-AIGC) as an
inexhaustible resource repository arises as a potential solution to address the
aforementioned challenge. Here we harness 1 million open-source synthetic
fundus images paired with natural language descriptions, to curate an ethical
language-image foundation model for retina image analysis named VisionCLIP.
VisionCLIP achieves competitive performance on three external datasets compared
with the existing method pre-trained on real-world data in a zero-shot fashion.
The employment of artificially synthetic images alongside corresponding textual
data for training enables the medical foundation model to successfully
assimilate knowledge of disease symptomatology, thereby circumventing potential
breaches of patient confidentiality.

利用 100 万个开放源合成的眼底图像与自然语言描述，构建具有伦理基础的语言 - 图像模型 VisionCLIP，进行视网膜图像分析，并在零样本情况下在三个外部数据集上取得了竞争性能。