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ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features
Detecting anomalies in fundus images through unsupervised methods is a challenging task due to the similarity between normal and abnormal tissues, as well as their indistinct boundaries.
Region and Spatial Aware Anomaly Detection for Fundus Images
ReSAD transfers a pre-trained model to extract the features of normal fundus images and applies the Region-and-Spatial-Aware feature Combination module (ReSC) for pixel-level features to build a memory bank.
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