Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals

TL;DR

Attri-Net is an inherently interpretable multi-label classification model that uses class-specific counterfactual attribution maps and linear classifiers to provide both local and global explanations, aligning with human knowledge without sacrificing accuracy.

Contribution

The paper introduces Attri-Net, a novel inherently interpretable model that generates class-specific attribution maps for global and local explanations in multi-label classification.

Findings

High-quality explanations aligned with clinical knowledge

Maintains classification performance comparable to non-interpretable models

Provides both local and global interpretability mechanisms

Abstract

Interpretability is crucial for machine learning algorithms in high-stakes medical applications. However, high-performing neural networks typically cannot explain their predictions. Post-hoc explanation methods provide a way to understand neural networks but have been shown to suffer from conceptual problems. Moreover, current research largely focuses on providing local explanations for individual samples rather than global explanations for the model itself. In this paper, we propose Attri-Net, an inherently interpretable model for multi-label classification that provides local and global explanations. Attri-Net first counterfactually generates class-specific attribution maps to highlight the disease evidence, then performs classification with logistic regression classifiers based solely on the attribution maps. Local explanations for each prediction can be obtained by interpreting the attribution maps weighted by the classifiers' weights. Global explanation of whole model can be obtained by jointly considering learned average representations of the attribution maps for each class (called the class centers) and the weights of the linear classifiers. To ensure the model is ``right for the right reason", we further introduce a mechanism to guide the model's explanations to align with human knowledge. Our comprehensive evaluations show that Attri-Net can generate high-quality explanations consistent with clinical knowledge while not sacrificing classification performance.