Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images

Concepts de base

Adapting CLIP models for medical anomaly detection improves generalization and performance significantly.

Résumé

This paper introduces a novel framework to repurpose the CLIP model for medical anomaly detection. The multi-level adaptation approach enhances visual features for improved generalization across various medical data types. Experiments show significant improvements over state-of-the-art models in anomaly classification and segmentation under zero-shot and few-shot scenarios.

Introduction

Recent advancements in large-scale visual-language pre-trained models have led to progress in zero-/few-shot anomaly detection.
Domain divergence between natural and medical images limits effectiveness in medical anomaly detection.

Methodology

Lightweight multi-level adaptation framework introduced to repurpose CLIP model for medical anomaly detection.
Multi-level, pixel-wise visual-language feature alignment loss functions guide the adaptation process.

Experiments

Experiments on challenging medical AD benchmarks demonstrate significant improvement over current state-of-the-art models.
Average AUC improvement of 6.24% and 7.33% for anomaly classification, 2.03% and 2.37% for anomaly segmentation under zero-shot and few-shot settings, respectively.

Related Works

Vanilla methods focus on unsupervised AD with normal images only.
Zero-/few-shot methods aim to achieve model generalization with limited training data.

Conclusion

The proposed method outperforms existing approaches on zero-/few-shot AC and AS tasks, showing promise for future research.

Stats

Our method significantly surpasses current state-of-the-art models, with an average AUC improvement of 6.24% and 7.33% for anomaly classification, 2.03% and 2.37% for anomaly segmentation under the zero-shot and few-shot settings, respectively.

Citations

"Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models."
"Our approach integrates multiple residual adapters into the pre-trained visual encoder."

Idées clés tirées de

Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images

by Chaoqin Huan... à arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12570.pdf

Questions plus approfondies

How can this multi-level adaptation approach be applied to other domains beyond medical imaging

The multi-level adaptation approach utilized in medical imaging can be extended to various other domains beyond healthcare. For instance, it can be applied in industrial settings for anomaly detection in manufacturing processes or quality control. By adapting visual-language models with multi-level feature adapters, anomalies in production lines or machinery could be detected efficiently. Additionally, this approach could also find applications in security and surveillance systems for identifying unusual activities or objects in real-time.

What are potential limitations or drawbacks of adapting CLIP models for medical anomaly detection

While adapting CLIP models for medical anomaly detection offers significant benefits, there are potential limitations to consider. One drawback is the need for annotated data specific to each new medical modality or anatomical region encountered during training. This requirement may limit the scalability of the model across a wide range of medical imaging datasets without sufficient labeled examples. Additionally, domain shift between natural images and medical images could pose challenges in effectively transferring knowledge from pre-trained CLIP models to the healthcare domain.

How might the concept of universal AD models impact the future of diagnostic technologies

The concept of universal anomaly detection (AD) models has the potential to revolutionize diagnostic technologies by providing versatile solutions that can adapt to diverse data types and scenarios. These universal AD models could enhance early detection of abnormalities across various fields such as healthcare, manufacturing, cybersecurity, and more. By enabling zero-shot or few-shot learning capabilities, these models offer flexibility and efficiency in detecting anomalies even when limited labeled data is available. The future impact of universal AD models includes improved accuracy in diagnostics, reduced false positives/negatives rates, and enhanced decision-making support systems across industries.