A Data-driven Approach to Referable Diabetic Retinopathy Detection
Conclusion: Additional boost strategies can improve performance substantially, but it is important to evaluate whether the additional (computation- and implementation-) complexity of each improvement is worth its benefits. We also corroborate that novel families of data-driven methods are the state of the art for diabetic retinopathy screening. Significance: By learning powerful discriminative patterns directly from available training retinal images, it is possible to perform referral diagnostics without detecting individual lesions.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research
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