Rad4XCNN: a new agnostic method for post-hoc global explanation of CNN-derived features by means of radiomics

TL;DR

Rad4XCNN is a novel method that combines CNN features with radiomics to provide interpretable, accurate, and global explanations for medical image classification, addressing the explainability-accuracy trade-off.

Contribution

The paper introduces Rad4XCNN, a new approach that enhances CNN interpretability using radiomics without sacrificing predictive accuracy in medical imaging.

Findings

Rad4XCNN maintains high accuracy comparable to CNNs.

Traditional saliency map explanations have limitations.

Rad4XCNN offers global explanations for clinical insights.

Abstract

In recent years, machine learning-based clinical decision support systems (CDSS) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI models poses significant challenges, particularly in medical contexts where reliability is a mandatory aspect. However, it appears that explainability is inversely proportional to accuracy. For this reason, achieving transparency without compromising predictive accuracy remains a key challenge. This paper presents a novel method, namely Rad4XCNN, to enhance the predictive power of CNN-derived features with the inherent interpretability of radiomic features. Rad4XCNN diverges from conventional methods based on saliency maps, by associating intelligible meaning to CNN-derived features by means of Radiomics, offering new perspectives on explanation methods beyond visualization maps. Using a breast cancer classification task as a case study, we evaluated Rad4XCNN on ultrasound imaging datasets, including an online dataset and two in-house datasets for internal and external validation. Some key results are: i) CNN-derived features guarantee more robust accuracy when compared against ViT-derived and radiomic features; ii) conventional visualization map methods for explanation present several pitfalls; iii) Rad4XCNN does not sacrifice model accuracy for their explainability; iv) Rad4XCNN provides a global explanation enabling the physician to extract global insights and findings. Our method can mitigate some concerns related to the explainability-accuracy trade-off. This study highlighted the importance of proposing new methods for model explanation without affecting their accuracy.