A Survey on Causal Representation Learning and Future Work for Medical Image Analysis

TL;DR

This survey reviews recent advances in Causal Representation Learning (CRL) for vision tasks, emphasizing its potential to improve medical image analysis by addressing out-of-distribution data and confounders.

Contribution

It provides a comprehensive overview of CRL theories and applications in vision, and proposes future research directions for medical image analysis.

Findings

CRL enhances robustness to out-of-distribution data.

Invariant risk minimization is a key theoretical framework.

CRL shows promise in transfer learning for medical images.

Abstract

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.