This paper proposes a method for automatic classification of spectral domain OCT data for the identification of patients with retinal diseases such as Diabetic Macular Edema (DME). We address this issue as an anomaly detection problem and propose a method that not only allows the classification of the OCT volume, but also allows the identification of the individual sensitivity and a specificity of 80% and 93% on the first dataset, and 100%and 80% on the second one. Moreover, the experiments show that the proposed method achieves better classification performance than other recently published works.
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