Learning disentangled representations in the imaging domain.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computer Vision

Background:

• Supervised learning often requires large labeled datasets.
• Generalizable representations are crucial for efficient AI model adaptation.
• Disentangled representation learning offers a solution to data limitations.

Purpose of the Study:

• To introduce the concept and necessity of disentangled representations.
• To review foundational concepts and practical methods for learning disentangled representations.
• To explore applications in medical imaging and computer vision.

Main Methods:

• Surveying existing literature on disentangled representation learning.
• Describing building blocks and criteria for learning disentangled representations.
• Analyzing exemplar works in medical imaging and computer vision.

Main Results:

• Disentangled representations can be fine-tuned with limited data.
• These representations enable direct application in unseen domains.
• Significant performance gains are achievable in target tasks.

Conclusions:

• Disentangled representation learning alleviates data and annotation requirements.
• It holds significant promise for computer vision and healthcare.
• Further research is needed to address limitations and challenges.