Exploring self-supervised learning biases for microscopy image representation

Area of Science:

• Computer Vision
• Microscopy Imaging
• Machine Learning

Background:

• Self-supervised representation learning (SSRL) uses image transformations to learn features.
• The impact of transformation choice on SSRL, particularly in microscopy, is under-explored.
• Transformations can introduce biases or act as beneficial supervision.

Purpose of the Study:

• To investigate the influence of image transformation design on SSRL in microscopy.
• To understand how transformations affect feature clustering and relevance based on class labels.
• To demonstrate the benefits of strategic transformation selection for improved classification.

Main Methods:

• Focusing on microscopy images with subtle cell phenotype differences.
• Analyzing the impact of various image transformations on learned representations.
• Evaluating classification performance and representation quality with different transformation strategies.

Main Results:

• Transformation design significantly impacts representation quality and feature clustering in microscopy.
• Imperceptible biases are introduced by transformations, varying with class labels.
• Strategic transformation selection enhances classification accuracy and representation quality, even with limited data.

Conclusions:

• Transformation design in SSRL is a critical factor, acting as implicit supervision.
• Careful selection of transformations is essential for effective feature learning in microscopy.
• Optimized transformations lead to superior performance in classification tasks with limited samples.