  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.

Image and Video Processing