Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature Extractor

• URL: http://arxiv.org/abs/2501.12524v1
• Date: Tue, 21 Jan 2025 22:28:22 GMT
• Title: Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature Extractor
• Authors: Jiaqi Guo, Yunnan Wu, Evangelos Kaimakamis, Georgios Petmezas, Vasileios E. Papageorgiou, Nicos Maglaveras, Aggelos K. Katsaggelos,
• Abstract summary: MeDiVLAD is a novel pipeline for multi-level lung-ultrasound severity scoring. We leverage self-knowledge distillation to pretrain a vision transformer (ViT) without label and aggregate frame-level features. We show that with minimal finetuning, MeDiVLAD outperforms conventional fully-supervised methods in both frame- and video-level scoring.
• Score: 11.77784387664675
• License:
• 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.

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