Surpassing state of the art on AMD area estimation from RGB fundus images through careful selection of U-Net architectures and loss functions for class imbalance
PositiveArtificial Intelligence
A recent study has made significant strides in detecting age-related macular degeneration (AMD) using RGB fundus images, a non-invasive imaging method. By carefully selecting U-Net architectures and loss functions to address class imbalance, researchers have surpassed previous benchmarks set by the ADAM challenge, which is the largest competition focused on AMD detection. This advancement is crucial as AMD is a leading cause of vision loss in older adults, and improving detection methods can lead to better patient outcomes.
— Curated by the World Pulse Now AI Editorial System



