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

arXiv: https://arxiv.org/abs/2404.16754 · HTML: https://mlsanity.com/paper/2404.16754/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [Med-Flamingo: a Multimodal Medical Few-shot Learner](https://mlsanity.com/paper/2307.15189/) (2023)
- [Towards Generalist Biomedical AI](https://mlsanity.com/paper/2307.14334/) (2023)
- [Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography](https://mlsanity.com/paper/2403.17834/) (2024)
- [Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data](https://mlsanity.com/paper/2308.02463/) (2023)
- [Large-Vocabulary Segmentation for Medical Images with Text Prompts](https://mlsanity.com/paper/2312.17183/) (2023)
- [Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering](https://mlsanity.com/paper/2307.11986/) (2023)
- [Exploring the Boundaries of GPT-4 in Radiology](https://mlsanity.com/paper/2310.14573/) (2023)

## Built on by (most cited first, 30 of 42)

- [MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine](https://mlsanity.com/paper/2408.02900/) (2024)
- [GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis](https://mlsanity.com/paper/2411.16778/) (2024)
- [Large Language Model with Region-guided Referring and Grounding for CT Report Generation](https://mlsanity.com/paper/2411.15539/) (2024)
- [AutoRG-Brain: Grounded Report Generation for Brain MRI](https://mlsanity.com/paper/2407.16684/) (2024)
- [Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks](https://mlsanity.com/paper/2410.18387/) (2024)
- [CT-Agent: A Multimodal-LLM Agent for 3D CT Radiology Question Answering](https://mlsanity.com/paper/2505.16229/) (2025)
- [Read Like a Radiologist: Efficient Vision-Language Model for 3D Medical Imaging Interpretation](https://mlsanity.com/paper/2412.13558/) (2024)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports](https://mlsanity.com/paper/2507.22030/) (2025)
- [MM-Skin: Enhancing Dermatology Vision-Language Model with an Image-Text Dataset Derived from Textbooks](https://mlsanity.com/paper/2505.06152/) (2025)
- [Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question Answering](https://mlsanity.com/paper/2505.18915/) (2025)
- [Argus: Benchmarking and Enhancing Vision-Language Models for 3D Radiology Report Generation](https://mlsanity.com/paper/2406.07146/) (2024)
- [UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation](https://mlsanity.com/paper/2504.21336/) (2025)
- [CT-GRAPH: Hierarchical Graph Attention Network for Anatomy-Guided CT Report Generation](https://mlsanity.com/paper/2508.05375/) (2025)
- [Region-Aware Multimodal Large Language Model via SlowFast Tokenization and Pseudo-Mask Guidance for 3D CT Report Generation](https://mlsanity.com/paper/2506.23102/) (2025)
- [3DReasonKnee: Advancing Grounded Reasoning in Medical Vision Language Models](https://mlsanity.com/paper/2510.20967/) (2025)
- [From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine](https://mlsanity.com/paper/2502.09242/) (2025)
- [3DMedAgent: Unified Perception-to-Understanding for 3D Medical Analysis](https://mlsanity.com/paper/2602.18064/) (2026)
- [MultiViewDx: Evidence-Linked Multi-View Clinical Diagnosis](https://mlsanity.com/paper/2410.14948/) (2024)
- [MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting](https://mlsanity.com/paper/2509.03800/) (2025)
- [ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding](https://mlsanity.com/paper/2607.24743/) (2026)
- [Enhancing Multi-task Learning Capability of Medical Generalist Foundation Model via Image-centric Multi-annotation Data](https://mlsanity.com/paper/2504.09967/) (2025)
- [Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach](https://mlsanity.com/paper/2506.03238/) (2025)
- [M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision](https://mlsanity.com/paper/2509.01360/) (2025)
- [Vision-Language Models for Automated 3D PET/CT Report Generation](https://mlsanity.com/paper/2511.20145/) (2025)
- [Cyclic Vision-Language Manipulator: Towards Reliable and Fine-Grained Image Interpretation for Automated Report Generation](https://mlsanity.com/paper/2411.05261/) (2024)
- [HSENet: Hybrid Spatial Encoding Network for 3D Medical Vision-Language Understanding](https://mlsanity.com/paper/2506.09634/) (2025)
- [MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports](https://mlsanity.com/paper/2506.19217/) (2025)
- [Medal S: Spatio-Textual Prompt Model for Medical Segmentation](https://mlsanity.com/paper/2511.13001/) (2025)
- [RADAR: A Multimodal Benchmark for 3D Image-Based Radiology Report Review](https://mlsanity.com/paper/2603.06681/) (2026)

## Abstract (arXiv)

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.