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• W2902000980 abstract "Ultrasound (US) image despeckling is a problem of high clinical importance. Machine learning solutions to the problem are considered impractical due to the unavailability of speckle-free US image dataset. On the other hand, the classical approaches, which are able to provide the desired outputs, have limitations like input dependent parameter tuning. In this work, a convolutional neural network (CNN) is developed which learns to remove speckle from US images using the outputs of these classical approaches. It is observed that the existing approaches can be combined in a complementary manner to generate an output better than their individual outputs. Thus, the CNN is trained using the individual outputs as well as the output ensembles. It eliminates the cumbersome process of parameter tuning required by the existing approaches for every new input. Further, the proposed CNN is able to outperform the state-of-the-art despeckling approaches and produces the outputs even better than the ensembles for certain images." @default.
• W2902000980 created "2018-12-11" @default.
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• W2902000980 date "2018-08-01" @default.
• W2902000980 modified "2023-09-26" @default.
• W2902000980 title "Despeckling CNN with Ensembles of Classical Outputs" @default.
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• W2902000980 doi "https://doi.org/10.1109/icpr.2018.8545031" @default.
• W2902000980 hasPublicationYear "2018" @default.
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