Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model

Oh, Kwanseok; Yoon, Jee Seok; Suk, Heung-Il

doi:10.48550/arxiv.2108.09451

Cited by 2 publications

(1 citation statement)

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“…One way to achieve semantic augmentation is to train a deep generative model to create counterfactuals, i.e., synthetic modifications of a sample such that some aspects of the original data remain unchanged [30,23,23,2,20,8]. However, these approaches mostly focus on the training stage of generative models and randomly generate samples for data augmentation, without considering which counterfactuals are more effective for downstream tasks.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification

Xia¹,

Sánchez²,

Chen³

et al. 2022

Preprint

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Data augmentation has been widely used in deep learning to reduce over-fitting and improve the robustness of models. However, traditional data augmentation techniques, e.g., rotation, cropping, flipping, etc., do not consider semantic transformations, e.g., changing the age of a brain image. Previous works tried to achieve semantic augmentation by generating counterfactuals, but they focused on how to train deep generative models and randomly created counterfactuals with the generative models without considering which counterfactuals are most effective for improving downstream training. Different from these approaches, in this work, we propose a novel adversarial counterfactual augmentation scheme that aims to find the most effective counterfactuals to improve downstream tasks with a pre-trained generative model. Specifically, we construct an adversarial game where we update the input conditional factor of the generator and the downstream classifier with gradient backpropagation alternatively and iteratively. The key idea is to find conditional factors that can result in hard counterfactuals for the classifier. This can be viewed as finding the 'weakness' of the classifier and purposely forcing it to overcome its weakness via the generative model. To demonstrate the effectiveness of the proposed approach, we validate the method with the classification of Alzheimer's Disease (AD) as the downstream task based on a pre-trained brain ageing synthesis model. We show the proposed approach improves test accuracy and can alleviate spurious correlations. Code will be released upon acceptance.

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Section: Introductionmentioning

confidence: 99%

Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification

Xia¹,

Sánchez²,

Chen³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Verma,

Boonsanong,

Hoang

et al. 2024

ACM Comput. Surv.

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Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understandable way, the relationship between the input and output of machine learning models is essential to the development of trustworthy machine learning based systems. A burgeoning body of research seeks to define the goals and methods of explainability in machine learning. In this paper, we seek to review and categorize research on counterfactual explanations , a specific class of explanation that provides a link between what could have happened had input to a model been changed in a particular way. Modern approaches to counterfactual explainability in machine learning draw connections to the established legal doctrine in many countries, making them appealing to fielded systems in high-impact areas such as finance and healthcare. Thus, we design a rubric with desirable properties of counterfactual explanation algorithms and comprehensively evaluate all currently proposed algorithms against that rubric. Our rubric provides easy comparison and comprehension of the advantages and disadvantages of different approaches and serves as an introduction to major research themes in this field. We also identify gaps and discuss promising research directions in the space of counterfactual explainability.

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Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model

Cited by 2 publications

References 40 publications

Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification

Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

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