Meta-learners for few-shot weakly-supervised optic disc and cup segmentation on fundus images

This article likely presents a novel approach to medical image analysis, specifically focusing on segmenting optic discs and cups in fundus images. The use of "few-shot" learning suggests the method aims to achieve good performance with limited labeled data, which is a common challenge in medical imaging. "Weakly-supervised" implies the method may rely on less precise or readily available labels, further enhancing its practicality. The term "meta-learners" indicates the use of algorithms that learn how to learn, potentially improving efficiency and adaptability. The source being ArXiv suggests this is a pre-print of a research paper.

• •Applies meta-learning to few-shot, weakly-supervised optic disc and cup segmentation.
• •Addresses the challenge of limited labeled data in medical image analysis.
• •Uses fundus images, relevant to ophthalmology.