Keywords: Analysis/Processing, Brain, Unsupervised Anomaly Detection
Motivation: Unsupervised anomaly detection (UAD) approaches on T1 contrast-enhanced (T1CE) images are currently not feasible as T1CE images of healthy individuals are not typically available.
Goal(s): In this work, we aim to eliminate the need for large labeled datasets that are required for manual anomaly detection on T1CE images.
Approach: Using deep learning (DL), we synthesized healthy T1CE images from non-contrast images available in public datasets. We also synthesized healthy-anomalous paired images and forced the DL network to learn the healthy reconstruction. The anomalies were localized by subtracting the reconstruction from the input image.
Results: The proposed method achieves state-of-the art dice score coefficients.
Impact: This work opens up new avenues of research in unsupervised anomaly detection on T1CE images which has been infeasible due to lack of healthy post-contrast images. We also propose a novel contrastive learning paradigm using synthesis of healthy-anomalous image pairs.
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