  With the onset of the COVID-19 pandemic, ultrasound has emerged as an
effective tool for bedside monitoring of patients. Due to this, a large amount
of lung ultrasound scans have been made available which can be used for AI
based diagnosis and analysis. Several AI-based patient severity scoring models
have been proposed that rely on scoring the appearance of the ultrasound scans.
AI models are trained using ultrasound-appearance severity scores that are
manually labeled based on standardized visual features. We address the
challenge of labeling every ultrasound frame in the video clips. Our
contrastive learning method treats the video clip severity labels as noisy weak
severity labels for individual frames, thus requiring only video-level labels.
We show that it performs better than the conventional cross-entropy loss based
training. We combine frame severity predictions to come up with video severity
predictions and show that the frame based model achieves comparable performance
to a video based TSM model, on a large dataset combining public and private
sources.
