Skip to main content
  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
Poster 11 Oct 2023

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.

More Like This

  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
01 Feb 2024

P4.15-Attention Mechanism

1.00 pdh 0.10 ceu
  • SPS
    Members: Free
    IEEE Members: Free
    Non-members: Free