ADFA: Attention-Augmented Differentiable Top-K Feature Adaptation for Unsupervised Medical Anomaly Detection

The scarcity of annotated data, particularly for rare diseases, limits the variability of training data and the range of detectable lesions, presenting a significant challenge for supervised anomaly detection in medical imaging. To solve this problem, we propose a novel unsupervised method for medic...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 206 - 210
Main Authors Huang, Yiming, Liu, Guole, Luo, Yaoru, Yang, Ge
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
Anomaly detection
attention mechanism
Biomedical imaging
feature adaptation
Feature extraction
Image analysis
Lesions
Location awareness
medical image
Training data
unsupervised learning
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Summary:The scarcity of annotated data, particularly for rare diseases, limits the variability of training data and the range of detectable lesions, presenting a significant challenge for supervised anomaly detection in medical imaging. To solve this problem, we propose a novel unsupervised method for medical image anomaly detection: Attention-Augmented Differentiable top-k Feature Adaptation (ADFA). The method utilizes Wide-ResNet50-2 (WR50) network pre-trained on ImageNet to extract initial feature representations. To reduce the channel dimensionality while preserving relevant channel information, we employ an attention-augmented patch descriptor on the extracted features. We then apply differentiable top-k feature adaptation to train the patch descriptor, mapping the extracted feature representations to a new vector space, enabling effective detection of anomalies. Experiments show that ADFA outperforms state-of-the-art (SOTA) methods on multiple challenging medical image datasets, confirming its effectiveness in medical anomaly detection.
DOI:10.1109/ICIP49359.2023.10222528