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

作者：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 · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-66071-6 · 被引用次数：5 · 研究领域：Cell Image Analysis Techniques、AI in cancer detection、Generative Adversarial Networks and Image Synthesis

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. AIaided diagnosis is an exciting area of cancer research, however, large scale training is limited by the availability of imaging datasets. Here, the authors develop FreeTumor as a generative AI framework to develop realistic tumor images for clinical application.