Weight Space Correlation Analysis: Quantifying Feature Utilization in Deep Learning Models

TL;DR

This paper introduces Weight Space Correlation Analysis, a method to measure how much deep learning models rely on specific features, helping verify if models use relevant clinical information or rely on confounding metadata.

Contribution

The paper presents a novel, interpretable approach to quantify feature utilization in deep models, validated through detecting artificial shortcuts and applied to clinical prediction models.

Findings

Successfully detected artificially induced shortcut learning.

Confirmed models focus on clinically relevant features.

Models decouple from irrelevant acquisition factors.

Abstract

Deep learning models in medical imaging are susceptible to shortcut learning, relying on confounding metadata (e.g., scanner model) that is often encoded in image embeddings. The crucial question is whether the model actively utilizes this encoded information for its final prediction. We introduce Weight Space Correlation Analysis, an interpretable methodology that quantifies feature utilization by measuring the alignment between the classification heads of a primary clinical task and auxiliary metadata tasks. We first validate our method by successfully detecting artificially induced shortcut learning. We then apply it to probe the feature utilization of an SA-SonoNet model trained for Spontaneous Preterm Birth (sPTB) prediction. Our analysis confirmed that while the embeddings contain substantial metadata, the sPTB classifier's weight vectors were highly correlated with clinically relevant factors (e.g., birth weight) but decoupled from clinically irrelevant acquisition factors (e.g. scanner). Our methodology provides a tool to verify model trustworthiness, demonstrating that, in the absence of induced bias, the clinical model selectively utilizes features related to the genuine clinical signal.