用离散自监督让医疗影像模型更通用，少调参也能用。
DiSSECT: Structuring Transfer-Ready Medical Image Representations through Discrete Self-Supervision
- 通过多尺度离散量化构建特征瓶颈，强制模型学结构化特征。
- 在分类与分割任务中表现优异，低标签数据下仍高效。
- 适合医疗影像迁移学习，尤其对标注少的场景友好。
自监督学习（SSL）已成为医疗图像表征学习的重要范式，尤其在标注数据有限的场景下。然而，现有方法常依赖复杂架构、解剖先验或高度调优的增强策略，限制了其可扩展性和泛化能力。更关键的是，在胸片等解剖结构相似、病灶细微的模态中，模型易陷入捷径学习。本文提出DiSSECT——一种基于离散自监督的高效临床可迁移表征框架，将多尺度向量量化引入SSL流程，形成离散表征瓶颈，迫使模型学习重复性强、结构感知的特征，抑制视图特异性或低效模式。该方法在分类与分割任务上均取得优异表现，几乎无需微调，并在低标签条件下展现出极高标签效率。我们在多个公开医疗影像数据集上验证了DiSSECT，结果表明其鲁棒性与泛化能力优于当前主流方法。
原文摘要 · Abstract (English)
Self-supervised learning (SSL) has emerged as a powerful paradigm for medical image representation learning, particularly in settings with limited labeled data. However, existing SSL methods often rely on complex architectures, anatomy-specific priors, or heavily tuned augmentations, which limit their scalability and generalizability. More critically, these models are prone to shortcut learning, especially in modalities like chest X-rays, where anatomical similarity is high and pathology is subtle. In this work, we introduce DiSSECT -- Discrete Self-Supervision for Efficient Clinical Transferable Representations, a framework that integrates multi-scale vector quantization into the SSL pipeline to impose a discrete representational bottleneck. This constrains the model to learn repeatable, structure-aware features while suppressing view-specific or low-utility patterns, improving representation transfer across tasks and domains. DiSSECT achieves strong performance on both classification and segmentation tasks, requiring minimal or no fine-tuning, and shows particularly high label efficiency in low-label regimes. We validate DiSSECT across multiple public medical imaging datasets, demonstrating its robustness and generalizability compared to existing state-of-the-art approaches.
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