Search Results for author:
Found 4 papers, 1 papers with code
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.
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans
The combination of PET-based metabolic and CT-based anatomic information can contribute to better tumor segmentation results.
An evaluation of U-Net in Renal Structure Segmentation
Renal structure segmentation from computed tomography angiography~(CTA) is essential for many computer-assisted renal cancer treatment applications.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
