F3-Net可无缝处理缺失影像模态，一站式分割多种脑部病灶。
F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement
- 用零样本策略替代缺失模态，无需显式生成网络
- 多病种无须重训，平均Dice达0.94（胶质瘤）至0.79（卒中）
- 适合临床部署，对数据异质性与模态缺失鲁棒
F3-Net是一种基础模型，旨在解决临床医学图像分割中对完整多模态输入的依赖、泛化能力差和任务特异性弱等长期挑战。通过灵活的合成模态训练，F3-Net在缺少MRI序列时仍能保持强性能，采用零图像策略替代缺失模态，无需依赖显式合成网络，从而提升实际应用价值。其统一架构支持胶质瘤、转移瘤、卒中及白质病变的多病理分割，无需重新训练，优于需疾病特异性微调的CNN与Transformer模型。在BraTS 2021、BraTS 2024和ISLES 2022等多个数据集上评估，展现对域偏移与临床异质性的强鲁棒性。在整体病理数据集上，F3-Net在BraTS-GLI 2024上取得0.94的平均Dice相似系数（DSC），BraTS-MET 2024为0.82，BraTS 2021为0.94，ISLES 2022为0.79。该模型成为连接深度学习研究与临床落地的通用、可扩展解决方案。
原文摘要 · Abstract (English)
F3-Net is a foundation model designed to overcome persistent challenges in clinical medical image segmentation, including reliance on complete multimodal inputs, limited generalizability, and narrow task specificity. Through flexible synthetic modality training, F3-Net maintains robust performance even in the presence of missing MRI sequences, leveraging a zero-image strategy to substitute absent modalities without relying on explicit synthesis networks, thereby enhancing real-world applicability. Its unified architecture supports multi-pathology segmentation across glioma, metastasis, stroke, and white matter lesions without retraining, outperforming CNN-based and transformer-based models that typically require disease-specific fine-tuning. Evaluated on diverse datasets such as BraTS 2021, BraTS 2024, and ISLES 2022, F3-Net demonstrates strong resilience to domain shifts and clinical heterogeneity. On the whole pathology dataset, F3-Net achieves average Dice Similarity Coefficients (DSCs) of 0.94 for BraTS-GLI 2024, 0.82 for BraTS-MET 2024, 0.94 for BraTS 2021, and 0.79 for ISLES 2022. This positions it as a versatile, scalable solution bridging the gap between deep learning research and practical clinical deployment.
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