Calibration failure and limited-label recalibration of retinal image classifiers under dataset shift
A retinal-image classifier that appeared nearly flawless on its home dataset lost much of its accuracy when tested on images ...
A retinal-image classifier that appeared nearly flawless on its home dataset lost much of its accuracy when tested on images ...
Researchers at Xinjiang University developed BGAFS-UNet, a boundary-aware deep learning architecture that improves retinal vessel segmentation fidelity while openly documenting ...
A dual-branch deep learning framework called QICAF-Net borrows the mathematics of quantum states to fuse retinal image features, achieving competitive ...
Researchers combined diffusion-generated synthetic retinal images, contrastive self-supervised pre-training, and a five-model ensemble to reach 95.1 percent accuracy in diabetic ...
Researchers in India have built a hybrid graph neural network framework that converts retinal images into graph structures, achieving up ...
Researchers have developed MDG-Net, a retinal vessel segmentation network that abandons traditional skip connections in favor of a multilevel decoder ...
A new artificial intelligence system that reads the fine architecture of the human retina is drawing attention for its ability ...
In a groundbreaking study set to advance the field of medical imaging, researchers have unveiled a novel approach to enhance ...
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© 2026 Scienmag - Science Magazine