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<p>Advancements in medical imaging AI, particularly in 3D imaging, have been limited due to the scarcity of comprehensive datasets. We introduce CT-RATE, a public dataset that pairs 3D medical images with corresponding textual reports. CT-RATE comprises 25,692 non-contrast 3D chest CT scans from 21,304 unique patients. Each scan is accompanied by its corresponding radiology report. Leveraging CT-RATE, we develop CT-CLIP, a CT-focused contrastive language–image pretraining framework designed for broad applications without the need for task-specific training. We demonstrate how CT-CLIP can be used in multi-abnormality detection and case retrieval, and outperforms state-of-the-art fully supervised models across all key metrics. By combining CT-CLIP’s vision encoder with a pretrained large language model, we create CT-CHAT, a vision–language foundational chat model for 3D chest CT volumes. Fine-tuned on over 2.7 million question–answer pairs derived from the CT-RATE dataset, CT-CHAT underscores the necessity for specialized methods in 3D medical imaging. Collectively, the open-source release of CT-RATE, CT-CLIP and CT-CHAT not only addresses critical challenges in 3D medical imaging but also lays the groundwork for future innovations in medical AI and improved patient care.</p>

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Generalist foundation models from a multimodal dataset for 3D computed tomography

  • 期刊论文

  • 发表日期：

    2026年2月12日

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  • Ibrahim Ethem Hamamci,
  • Sezgin Er,
  • Chenyu Wang,
  • Furkan Almas,
  • Ayse Gulnihan Simsek,
  • Sevval Nil Esirgun,
  • Irem Dogan,
  • Omer Faruk Durugol,
  • Benjamin Hou,
  • Suprosanna Shit,
  • Weicheng Dai,
  • Murong Xu,
  • Hadrien Reynaud,
  • Muhammed Furkan Dasdelen,
  • Bastian Wittmann,
  • Tamaz Amiranashvili,
  • Enis Simsar,
  • Mehmet Simsar,
  • Emine Bensu Erdemir,
  • Abdullah Alanbay,
  • Anjany Sekuboyina,
  • Berkan Lafci,
  • Ahmet Kaplan,
  • Zhiyong Lu,
  • Malgorzata Polacin,
  • Bernhard Kainz,
  • Christian Bluethgen,
  • Kayhan Batmanghelich,
  • Mehmet Kemal Ozdemir,
  • Bjoern Menze

摘要

Advancements in medical imaging AI, particularly in 3D imaging, have been limited due to the scarcity of comprehensive datasets. We introduce CT-RATE, a public dataset that pairs 3D medical images with corresponding textual reports. CT-RATE comprises 25,692 non-contrast 3D chest CT scans from 21,304 unique patients. Each scan is accompanied by its corresponding radiology report. Leveraging CT-RATE, we develop CT-CLIP, a CT-focused contrastive language–image pretraining framework designed for broad applications without the need for task-specific training. We demonstrate how CT-CLIP can be used in multi-abnormality detection and case retrieval, and outperforms state-of-the-art fully supervised models across all key metrics. By combining CT-CLIP’s vision encoder with a pretrained large language model, we create CT-CHAT, a vision–language foundational chat model for 3D chest CT volumes. Fine-tuned on over 2.7 million question–answer pairs derived from the CT-RATE dataset, CT-CHAT underscores the necessity for specialized methods in 3D medical imaging. Collectively, the open-source release of CT-RATE, CT-CLIP and CT-CHAT not only addresses critical challenges in 3D medical imaging but also lays the groundwork for future innovations in medical AI and improved patient care.