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Automated Segmentation of Computed Tomography Images with Submanifold Sparse Convolutional Networks
Quantitative cancer image analysis relies on the accurate delineation of tumours, a very specialised and time-consuming task.
Image-based model parameter optimization using Model-Assisted Generative Adversarial Networks
We propose and demonstrate the use of a model-assisted generative adversarial network (GAN) to produce fake images that accurately match true images through the variation of the parameters of the model that describes the features of the images.
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