Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview Learning for Medical Image Segmentation. (arXiv:2311.01023v1 [cs.CV])

The utilisation of deep learning segmentation algorithms that learn complex
organs and tissue patterns and extract essential regions of interest from the
noisy background to improve the visual ability for medical image diagnosis has
achieved impressive results in Medical Image Computing (MIC). This thesis
focuses on retinal blood vessel segmentation tasks, providing an extensive
literature review of deep learning-based medical image segmentation approaches
while comparing the methodologies and empirical performances. The work also
examines the limitations of current state-of-the-art methods …

algorithms arxiv augmentation auto computing deep learning diagnosis extract image learn medical patterns segmentation thesis visual