Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Biomedical Imaging

Background:

• High-quality annotated data is scarce in machine learning, particularly for complex biomedical segmentation tasks.
• Expert annotation is time-consuming, necessitating methods to reduce this effort.

Purpose of the Study:

• To evaluate the applicability of Self-Supervised Learning (SSL) for biomedical segmentation with limited datasets.
• To provide a comprehensive qualitative and quantitative analysis of SSL methods in this domain.

Main Methods:

• Conducted a comprehensive evaluation of various SSL methods on biomedical imaging segmentation tasks.
• Introduced novel application-specific metrics alongside standard evaluation metrics.
• Developed a software package containing all metrics and state-of-the-art methods for direct application.

Main Results:

• SSL demonstrated performance improvements of up to 10% in segmentation tasks.
• These improvements are particularly notable for methods specifically designed for segmentation.

Conclusions:

• SSL is a valuable approach for data-efficient learning in biomedical applications, mitigating the need for extensive manual annotation.
• The extensive evaluation pipeline highlights significant differences between SSL approaches, emphasizing the need for careful selection.