无需标注数据，自动识别CT中的异常病灶
Screener: Self-supervised Pathology Segmentation in Medical CT Images
- 用自监督学习提取特征，无需人工标注预训练
- 在3万份无标签CT上训练，4个数据集表现最优
- 适合医疗影像异常检测，尤其标注困难场景
3D医学影像中准确检测所有病灶仍具挑战，因有监督模型仅能识别已有数据集中标注的少数病种。为此，本文将病灶检测建模为无监督视觉异常分割（UVAS）问题，利用病灶相对于健康组织的罕见性。提出两项创新：（1）密集自监督学习用于特征提取，避免依赖有监督预训练；（2）采用可学习、遮挡不变的密集特征作为条件变量，替代手工设计的位置编码。模型Screener在超过3万份未标注的3D CT体积上训练，在包含1820例扫描的四个大规模测试数据集上优于现有UVAS方法。此外，在有监督微调设置下，Screener超越现有自监督预训练方法，成为病灶分割的先进基础模型。代码与预训练模型将公开。
原文摘要 · Abstract (English)
Accurate detection of all pathological findings in 3D medical images remains a significant challenge, as supervised models are limited to detecting only the few pathology classes annotated in existing datasets. To address this, we frame pathology detection as an unsupervised visual anomaly segmentation (UVAS) problem, leveraging the inherent rarity of pathological patterns compared to healthy ones. We enhance the existing density-based UVAS framework with two key innovations: (1) dense self-supervised learning for feature extraction, eliminating the need for supervised pretraining, and (2) learned, masking-invariant dense features as conditioning variables, replacing hand-crafted positional encodings. Trained on over 30,000 unlabeled 3D CT volumes, our fully self-supervised model, Screener, outperforms existing UVAS methods on four large-scale test datasets comprising 1,820 scans with diverse pathologies. Furthermore, in a supervised fine-tuning setting, Screener surpasses existing self-supervised pretraining methods, establishing it as a state-of-the-art foundation for pathology segmentation. The code and pretrained models will be made publicly available.
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