3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in fovea-detection-11
Use these libraries to find fovea-detection-11 models and implementations
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The proposed HBA-U-Net network consists of a novel bottleneck attention block that combines and refines self-attention, channel attention, and relative-position attention to highlight retinal abnormalities that may be important for fovea and OD segmentation in the degenerated retina.
A novel method, named JOINED, for prior guided multi-task learning for joint OD/OC segmentation and fovea detection, which outperforms existing state-of-the-art approaches on the publicly-available GAMMA, PALM, and REFUGE datasets of fundus images.
Adding a benchmark result helps the community track progress.