Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals

TL;DR

Attri-Net is an inherently interpretable multi-label classification model for medical images that uses class-specific counterfactual attribution maps, providing trustworthy explanations while maintaining competitive accuracy.

Contribution

This paper introduces Attri-Net, a novel inherently interpretable multi-label classifier that generates class-specific explanations using counterfactuals, addressing limitations of post-hoc methods.

Findings

Attri-Net produces high-quality, clinically consistent explanations.

Attri-Net achieves classification performance comparable to state-of-the-art models.

The approach outperforms existing post-hoc explanation techniques in multi-label scenarios.

Abstract

Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks do not provide explanations for their predictions, which can lead to mistrust and suboptimal human-ML collaboration. Post-hoc explanation techniques, which are widely used in practice, have been shown to suffer from severe conceptual problems. Furthermore, as we show in this paper, current explanation techniques do not perform adequately in the multi-label scenario, in which multiple medical findings may co-occur in a single image. We propose Attri-Net, an inherently interpretable model for multi-label classification. Attri-Net is a powerful classifier that provides transparent, trustworthy, and human-understandable explanations. The model first generates class-specific attribution maps based on counterfactuals to identify which image regions correspond to certain medical findings. Then a simple logistic regression classifier is used to make predictions based solely on these attribution maps. We compare Attri-Net to five post-hoc explanation techniques and one inherently interpretable classifier on three chest X-ray datasets. We find that Attri-Net produces high-quality multi-label explanations consistent with clinical knowledge and has comparable classification performance to state-of-the-art classification models.