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- W2893531431 abstract "Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset, especially when one is dealing with medical images. For supervised image analysis in medical imaging, having image data along with their corresponding annotated ground-truths is costly as well as time consuming since annotations of the data is done by medical experts manually. In this paper, we propose a new Generative Adversarial Network for Medical Imaging (MI-GAN). The MI-GAN generates synthetic medical images and their segmented masks, which can then be used for the application of supervised analysis of medical images. Particularly, we present MI-GAN for synthesis of retinal images. The proposed method generates precise segmented images better than the existing techniques. The proposed model achieves a dice coefficient of 0.837 on STARE dataset and 0.832 on DRIVE dataset which is state-of-the-art performance on both the datasets." @default.
- W2893531431 created "2018-10-05" @default.
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- W2893531431 date "2018-10-12" @default.
- W2893531431 modified "2023-10-18" @default.
- W2893531431 title "Generative Adversarial Network for Medical Images (MI-GAN)" @default.
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- W2893531431 doi "https://doi.org/10.1007/s10916-018-1072-9" @default.
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