Weakly Semi-Supervised Detection in Lung Ultrasound Videos

TL;DR

This paper introduces a weakly supervised learning method for detecting lung consolidations in ultrasound videos, reducing the need for detailed annotations and improving detection accuracy and efficiency.

Contribution

It extends a teacher-student framework with video-level supervision and adaptive pseudo-labeling to enhance medical video object detection.

Findings

Improved detection accuracy over baseline semi-supervised models

Enhanced robustness in lung consolidation detection

Reduced annotation effort and data requirements

Abstract

Frame-by-frame annotation of bounding boxes by clinical experts is often required to train fully supervised object detection models on medical video data. We propose a method for improving object detection in medical videos through weak supervision from video-level labels. More concretely, we aggregate individual detection predictions into video-level predictions and extend a teacher-student training strategy to provide additional supervision via a video-level loss. We also introduce improvements to the underlying teacher-student framework, including methods to improve the quality of pseudo-labels based on weak supervision and adaptive schemes to optimize knowledge transfer between the student and teacher networks. We apply this approach to the clinically important task of detecting lung consolidations (seen in respiratory infections such as COVID-19 pneumonia) in medical ultrasound videos. Experiments reveal that our framework improves detection accuracy and robustness compared to baseline semi-supervised models, and improves efficiency in data and annotation usage.