Abstract
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.
Abstract (translated)
随着COVID-19疫情的爆发，超声成像因其非侵入性、经济性和便携性而成为一种有前景的COVID-19检测技术。对此，研究人员专注于开发基于AI的支持实时诊断的评分系统。然而，公开可用的超声数据集规模有限且标注不充分，这给训练稳健的AI模型带来了重大挑战。本文提出了一种名为MeDiVLAD的新颖管道，旨在解决多级肺部超声（LUS）严重程度评分中的上述问题。具体来说，我们利用自我知识蒸馏来在无标签的情况下预训练视觉变换器（ViT），并通过双层VLAD聚合技术聚集帧级别的特征。结果显示，在仅进行少量微调的情况下，MeDiVLAD在帧级和视频级评分方面均优于传统的完全监督方法，并且能够提供质量卓越的分类推理能力。这种优越性能使得诸如自动识别关键肺部病理区域等重要应用成为可能，同时也为更广泛的医学视频分类任务提供了稳健解决方案。
URL
https://arxiv.org/abs/2501.12524
