Poster
in
Affinity Workshop: Black in AI
Stronger is not better: Better Augmentations in Contrastive Learning for Medical Image Segmentation
Azeez Idris · Abdurahman Ali Mohammed · Samuel Fanijo
Keywords: [ machine learning ] [ Deep Learning ] [ Computer Vision ] [ Applications of AI to Health ]
Self-supervised contrastive learning is among the recent representation learning methods that have shown performance gains in several downstream tasks including semantic segmentation. This paper evaluates strong data augmentation, one of the most important components for self-supervised contrastive learning's improved performance. Strong data augmentation involves applying the composition of multiple augmentation techniques on images. Surprisingly, we find that the existing data augmentations do not always improve performance for semantic segmentation for medical images. We experiment with other augmentations that provide improved performance.