用自蒸馏视觉变压器提升小样本肺超声严重程度评分
Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature Extractor
- 自蒸馏预训练ViT+双层VLAD特征聚合，减少对标注数据依赖
- 微调后在帧级和视频级评分上超越全监督方法
- 适合医疗影像少样本场景，可解释性强
新冠疫情推动超声成像在疾病检测中的应用，因其无创、低成本和便携性。然而，公开的超声数据集规模小且标注不充分，制约了AI模型的训练。本文提出MeDiVLAD，一种针对多层级肺超声（LUS）严重程度评分的新方法。通过自知识蒸馏预训练视觉变换器（ViT）并无需标签，再采用双层VLAD聚合帧级特征。实验表明，经少量微调后，MeDiVLAD在帧级和视频级评分任务中均优于传统全监督方法，并具备高质量分类推理能力。该性能支持关键病理区域自动识别，为更广泛的医学视频分类任务提供稳健解决方案。
原文摘要 · Abstract (English)
With the advent of the COVID-19 pandemic, ultrasound imaging has emerged as a promising technique for COVID-19 detection, due to its non-invasive nature, affordability, and portability. In response, researchers have focused on developing AI-based scoring systems to provide real-time diagnostic support. However, the limited size and lack of proper annotation in publicly available ultrasound datasets pose significant challenges for training a robust AI model. This paper proposes MeDiVLAD, a novel pipeline to address the above issue for multi-level lung-ultrasound (LUS) severity scoring. In particular, we leverage self-knowledge distillation to pretrain a vision transformer (ViT) without label and aggregate frame-level features via dual-level VLAD aggregation. We show that with minimal finetuning, MeDiVLAD outperforms conventional fully-supervised methods in both frame- and video-level scoring, while offering classification reasoning with exceptional quality. This superior performance enables key applications such as the automatic identification of critical lung pathology areas and provides a robust solution for broader medical video classification tasks.
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