InvCoSS：医学多模态图像预训练中反演驱动的持续自监督学习
InvCoSS: Inversion-driven Continual Self-supervised Learning in Medical Multi-modal Image Pre-training
用模型反演生成近似旧任务分布的合成图像替代真实数据回放，为视觉预训练中的持续学习提供了一条不依赖跨站点数据共享、且显著降低存储的可行路径；InvUNet 的多尺度高低频融合与无类别引导的排斥式表征学习可直接迁移到其他需要合成回放的增量预训练场景。
原文依据 InvCoSS inverts the pre-trained self-supervised model to generate synthetic images that approximate the original training distribution
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论医学影像中的持续自监督学习（CSSL）按顺序训练基础模型，既免除了为联合训练而收集多模态图像的需求，又能在保护数据隐私的同时对下游性能带来可观的提升。然而，现有方法大多仍依赖回放先前阶段的数据来防止灾难性遗忘，这损害了隐私，也限制了其在对跨站点数据传输常有严格限制的真实场景中的适用性。在本工作中，我们提出 InvCoSS，一个面向医学多模态图像预训练的反演驱动持续自监督学习框架。具体而言，在完成先前任务的训练后，InvCoSS 对预训练的自监督模型进行反演，生成近似原始训练分布的合成图像。这些合成图像随后与新任务的数据结合进行联合优化，从而在严格遵循无法访问先前真实数据这一约束的前提下有效缓解灾难性遗忘。此外，为提升合成图像的保真度，我们引入一种新颖的 InvUNet，采用多尺度融合架构来恢复反演图像的高频与低频成分。为增强多样性并防止模式崩溃，我们设计了一种排斥式表征学习机制，在无类别引导的情况下促使合成图像形成多样化的特征空间。在九个下游任务上的大量实验验证了 InvCoSS 的有效性，其性能与先前的数据回放方法相当甚至更优，同时显著降低了存储需求并消除了数据隐私约束。
英文原文摘要 1724 字符
Continual self-supervised learning (CSSL) in medical imaging trains a foundation model sequentially, alleviating the need for collecting multi-modal images for joint training and offering promising improvements in downstream performance while preserving data privacy. However, most existing methods still rely on replaying data from previous stages to prevent catastrophic forgetting, which compromises privacy and limits their applicability in real-world scenarios where data transfer across sites is often restricted. In this work, we propose InvCoSS, an inversion-driven continual self-supervised learning framework for medical multi-modal image pre-training. Specifically, after training on a previous task, InvCoSS inverts the pre-trained self-supervised model to generate synthetic images that approximate the original training distribution. These synthetic images are then combined with data from the new task for joint optimization, which effectively mitigates catastrophic forgetting while strictly adhering to the constraint of no access to previous real data. Furthermore, to improve the fidelity of synthetic images, we introduce a novel InvUNet with a multi-scale fusion architecture to restore both high- and low-frequency components of the inverted images. To enhance diversity and prevent mode collapse, we design a repulsive representation-learning mechanism that encourages a diverse feature space for synthetic images without class guidance. Extensive experiments across nine downstream tasks validate the effectiveness of InvCoSS, achieving performance comparable to or even superior to prior data-replay methods while significantly reducing storage requirements and eliminating data privacy constraints.