A visual attention guided unsupervised feature learning for robust vessel delineation in retinal images
Conclusions The discriminative features learned via visual attention mechanism is superior to hand-crafted features, and it is easily adaptable to various kind of datasets where generous training images are often scarce. Hence, our approach can be easily integrated into large-scale retinal screening programs where the expensive labelled annotation is often unavailable.
Source: Biomedical Signal Processing and Control - Category: Biomedical Science Source Type: research
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