ADFA: Attention-augmented Differentiable top-k Feature Adaptation for
  Unsupervised Medical Anomaly Detection
Yiming Huang Guole Liu Yaoru Luo Ge Yang
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.