Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations

TL;DR

This paper introduces a novel selective test-time adaptation method using neural implicit representations to improve unsupervised anomaly detection in medical imaging, effectively enhancing detection accuracy across unseen domains without retraining the source model.

Contribution

It proposes a zero-shot, model-agnostic selective adaptation approach that enhances anomaly detection performance in unseen domains without modifying the original model.

Findings

Improves detection rates by up to 78% for enlarged ventricles.

Enhances detection accuracy for edemas by 24%.

Demonstrates effectiveness across multiple conditions and target distributions.

Abstract

Deep learning models in medical imaging often encounter challenges when adapting to new clinical settings unseen during training. Test-time adaptation offers a promising approach to optimize models for these unseen domains, yet its application in anomaly detection (AD) remains largely unexplored. AD aims to efficiently identify deviations from normative distributions; however, full adaptation, including pathological shifts, may inadvertently learn the anomalies it intends to detect. We introduce a novel concept of selective test-time adaptation that utilizes the inherent characteristics of deep pre-trained features to adapt selectively in a zero-shot manner to any test image from an unseen domain. This approach employs a model-agnostic, lightweight multi-layer perceptron for neural implicit representations, enabling the adaptation of outputs from any reconstruction-based AD method without altering the source-trained model. Rigorous validation in brain AD demonstrated that our strategy substantially enhances detection accuracy for multiple conditions and different target distributions. Specifically, our method improves the detection rates by up to 78% for enlarged ventricles and 24% for edemas.