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Found 3 papers, 2 papers with code
Learning self-calibrated optic disc and cup segmentation from multi-rater annotations
In this paper, we propose a novel neural network framework to learn OD/OC segmentation from multi-rater annotations.
Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification
Deep learning based medical imaging classification models usually suffer from the domain shift problem, where the classification performance drops when training data and real-world data differ in imaging equipment manufacturer, image acquisition protocol, patient populations, etc.
Opinions Vary? Diagnosis First!
Motivated by the observation that OD/OC segmentation is often used for the glaucoma diagnosis clinically, in this paper, we propose a novel strategy to fuse the multi-rater OD/OC segmentation labels via the glaucoma diagnosis performance.
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