Network Agnostic Knowledge Transfer from Latent Dataset for Medical Image Segmentation

Transfer learning often employs all or part of the weights of a pre-trained net-work to the problem at hand . This limits the flexibility of new neural architectures . We propose to transfer the knowledge of a neural network from a . latent dataset to another neural network (student) by training the student on a dataset agent whose annotations are generated by the teacher . This algorithm has the potential to be employed in novel applications where the teacher-training dataset is not accessible, particularly in medical applications . Extensive experiments on six multi-organ medical . image segmentation datasets have shown that the proposed algorithm was effective for knowledge transfer and easy to be used with fine-tuning . The proposed algorithm can be flexibly conducted between heterogeneous neural architecture 

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Keywords : neural - transfer - dataset - medical - algorithm -

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