Fig 7 - uploaded by Shekhar Suresh Chandra
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Example intermediate feature maps of a hip bone enhanced by WRS by incorporating information from other parallel branches.
Source publication
Deep neural networks are parameterised by weights that encode feature representations, whose performance is dictated through generalisation by using large-scale feature-rich datasets. The lack of large-scale labelled 3D medical imaging datasets restrict constructing such generalised networks. In this work, a novel 3D segmentation network, Fabric Im...
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Context 1
... can be observed across many of the feature maps. As per Fig. 7C, WRS may also "clean up" the input by subtracting "extraneous" signal intensity. In this case, WRS removed noise-like artefacts that share little correlation to a more "faithful" output of the underlying objects of interest. It was also observed that, in some cases, WRS leaves some of the activation maps ...
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... the activation maps before and after WRS are visualised and some samples are presented in Fig. 7. As per Fig. 7A, B and D, WRS is shown to augment (enhance the definition) of certain features by incorporating activations from parallel branches. As a result, more prominent shape outlines can be observed across many of the feature maps. As per Fig. 7C, WRS may also "clean up" the input by subtracting "extraneous" signal intensity. ...
Context 3
... the activation maps before and after WRS are visualised and some samples are presented in Fig. 7. As per Fig. 7A, B and D, WRS is shown to augment (enhance the definition) of certain features by incorporating activations from parallel branches. As a result, more prominent shape outlines can be observed across many of the feature maps. As per Fig. 7C, WRS may also "clean up" the input by subtracting "extraneous" signal intensity. In this case, WRS ...
Context 4
... the activation maps before and after WRS are visualised and some samples are presented in Fig. 7. As per Fig. 7A, B and D, WRS is shown to augment (enhance the definition) of certain features by incorporating activations from parallel branches. As a result, more prominent shape outlines can be observed across many of the feature maps. As per Fig. 7C, WRS may also "clean up" the input by subtracting "extraneous" signal intensity. In this case, WRS removed noise-like artefacts that share little correlation to a more "faithful" output of the underlying objects of interest. It was also observed that, in some cases, WRS leaves some of the activation maps ...
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