Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment Perspective

TL;DR

This paper introduces a novel image quality assessment metric called fusion quality, combining SSIM and l1 to improve anomaly detection in brain MRI by emphasizing regional variations and enhancing reconstruction evaluation.

Contribution

It proposes a new IQA metric and data transformation method that better captures subtle anomalies in brain MRI, outperforming existing metrics in anomaly detection tasks.

Findings

Fusion quality improves anomaly detection accuracy.

The method enhances regional variation sensitivity.

Experimental results show significant performance gains.

Abstract

Reconstruction-based methods, particularly those leveraging autoencoders, have been widely adopted for anomaly detection task in brain MRI. Unlike most existing works try to improve the task accuracy through architectural or algorithmic innovations, we tackle this task from image quality assessment (IQA) perspective, an under-explored direction in the field. Due to the limitations of conventional metrics such as l1 in capturing the nuanced differences in reconstructed images for medical anomaly detection, we propose fusion quality, a novel metric that wisely integrates the structure-level sensitivity of Structural Similarity Index Measure (SSIM) with the pixel-level precision of l1. The metric offers a more comprehensive assessment of reconstruction quality, considering intensity (subtractive property of l1 and divisive property of SSIM), contrast, and structural similarity. Furthermore, the proposed metric makes subtle regional variations more impactful in the final assessment. Thus, considering the inherent divisive properties of SSIM, we design an average intensity ratio (AIR)-based data transformation that amplifies the divisive discrepancies between normal and abnormal regions, thereby enhancing anomaly detection. By fusing the aforementioned two components, we devise the IQA approach. Experimental results on two distinct brain MRI datasets show that our IQA approach significantly enhances medical anomaly detection performance when integrated with state-of-the-art baselines.