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Memory-Augmented Anomaly Generative Adversarial Network for Retinal Oct Images Screening
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Memory-Augmented Anomaly Generative Adversarial Network for Retinal Oct Images Screening
Optical coherence tomography (OCT) plays an important role in retinal disease screening. Traditional classification-based screening methods require complicated annotation works. Due to the difficulty of collecting abnormal samples, some anomaly detection methods have been applied to screen retinal lesions only based on normal samples. However, most existing anomaly detection methods are time consuming and easily misjudging abnormal OCT images with implicit lesions like small drusen. To solve these problems, we propose a memory-augmented anomaly generative adversarial network (MA-GAN) for retinal OCT screening. Within the generator, we establish a memory module to enhance the detail expressing abilities of typical OCT normal patterns. Meanwhile, the discriminator of MA-GAN is decomposed orthogonally so that it has the encoding ability simultaneously. As a result, the abnormal image can be screened by the greater difference in the distribution of pixels and features between the original image and its reconstructed image. The model trained with 13000 normal OCT images reaches 0.875 AUC on the test set of 2000 normal images and 1000 anomalous images. And the inference time only takes 35 milliseconds for each image. Compared to other anomaly detection methods, our MA-GAN has the advantages in model accuracy and computation time for retinal OCT screening.
Optical coherence tomography (OCT) plays an important role in retinal disease screening. Traditional classification-based screening methods require complicated annotation works. Due to the difficulty of collecting abnormal samples, some anomaly detection methods have been applied to screen retinal lesions only based on normal samples. However, most existing anomaly detection methods are time consuming and easily misjudging abnormal OCT images with implicit lesions like small drusen. To solve these problems, we propose a memory-augmented anomaly generative adversarial network (MA-GAN) for retinal OCT screening. Within the generator, we establish a memory module to enhance the detail expressing abilities of typical OCT normal patterns. Meanwhile, the discriminator of MA-GAN is decomposed orthogonally so that it has the encoding ability simultaneously. As a result, the abnormal image can be screened by the greater difference in the distribution of pixels and features between the original image and its reconstructed image. The model trained with 13000 normal OCT images reaches 0.875 AUC on the test set of 2000 normal images and 1000 anomalous images. And the inference time only takes 35 milliseconds for each image. Compared to other anomaly detection methods, our MA-GAN has the advantages in model accuracy and computation time for retinal OCT screening.
