A Survey on Causal Representation Learning and Future Work for Medical Image Analysis
It provides a survey for researchers in machine learning and medical imaging, focusing on incremental compilation of existing CRL methods.
This survey addresses the problem of out-of-distribution data and confounders degrading model performance by reviewing Causal Representation Learning (CRL) advances in vision, including theoretical and practical work, and proposes future directions in medical image analysis and general theory.
Statistical machine learning algorithms have achieved state-of-the-art results on benchmark datasets, outperforming humans in many tasks. However, the out-of-distribution data and confounder, which have an unpredictable causal relationship, significantly degrade the performance of the existing models. Causal Representation Learning (CRL) has recently been a promising direction to address the causal relationship problem in vision understanding. This survey presents recent advances in CRL in vision. Firstly, we introduce the basic concept of causal inference. Secondly, we analyze the CRL theoretical work, especially in invariant risk minimization, and the practical work in feature understanding and transfer learning. Finally, we propose a future research direction in medical image analysis and CRL general theory.