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<p>AI-driven tumor recognition unlocks new possibilities for precise tumor screening and diagnosis. However, the progress is heavily hampered by the scarcity of annotated datasets, demanding extensive efforts by radiologists. To this end, we introduce <b>FreeTumor</b>, a Generative AI framework to enable large-scale tumor synthesis for mitigating data scarcity. Specifically, FreeTumor effectively leverages limited labeled data and large-scale unlabeled data for training. Unleashing the power of large-scale data, FreeTumor is capable of synthesizing a large number of realistic tumors for augmenting training datasets. We curate a large-scale dataset comprising 161,310 Computed Tomography (CT) volumes for tumor synthesis and recognition, with only 2.3% containing annotated tumors. 13 board-certified radiologists are engaged to discern between synthetic and real tumors, rigorously validating the quality of synthetic tumors. Through high-quality tumor synthesis, FreeTumor showcases a notable superiority over state-of-the-art tumor recognition methods, indicating promising prospects in clinical applications.</p>

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Large-scale generative tumor synthesis in computed tomography images for improving tumor recognition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月11日

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  • Linshan Wu,
  • Jiaxin Zhuang,
  • Yanning Zhou,
  • Sunan He,
  • Jiabo Ma,
  • Luyang Luo,
  • Xi Wang,
  • Xuefeng Ni,
  • Xiaoling Zhong,
  • Mingxiang Wu,
  • Yinghua Zhao,
  • Xiaohui Duan,
  • Varut Vardhanabhuti,
  • Pranav Rajpurkar,
  • Hao Chen

摘要

AI-driven tumor recognition unlocks new possibilities for precise tumor screening and diagnosis. However, the progress is heavily hampered by the scarcity of annotated datasets, demanding extensive efforts by radiologists. To this end, we introduce FreeTumor, a Generative AI framework to enable large-scale tumor synthesis for mitigating data scarcity. Specifically, FreeTumor effectively leverages limited labeled data and large-scale unlabeled data for training. Unleashing the power of large-scale data, FreeTumor is capable of synthesizing a large number of realistic tumors for augmenting training datasets. We curate a large-scale dataset comprising 161,310 Computed Tomography (CT) volumes for tumor synthesis and recognition, with only 2.3% containing annotated tumors. 13 board-certified radiologists are engaged to discern between synthetic and real tumors, rigorously validating the quality of synthetic tumors. Through high-quality tumor synthesis, FreeTumor showcases a notable superiority over state-of-the-art tumor recognition methods, indicating promising prospects in clinical applications.