Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature Extractor

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.

@article{guo2025_2501.12524,
  title={ Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature Extractor },
  author={ Jiaqi Guo and Yunan Wu and Evangelos Kaimakamis and Georgios Petmezas and Vasileios E. Papageorgiou and Nicos Maglaveras and Aggelos K. Katsaggelos },
  journal={arXiv preprint arXiv:2501.12524},
  year={ 2025 }
}