Focusnet: An attention-based fully convolutional network for medical image segmentation
2019 IEEE 16th international symposium on biomedical imaging (ISBI …, 2019•ieeexplore.ieee.org
We propose a novel technique to incorporate attention within convolutional neural networks
using feature maps generated by a separate convolutional autoencoder. Our attention
architecture is well suited for incorporation with deep convolutional networks. We evaluate
our model on benchmark segmentation datasets in skin cancer segmentation and lung
lesion segmentation. Results show highly competitive performance when compared with U-
Net and it's residual variant.
using feature maps generated by a separate convolutional autoencoder. Our attention
architecture is well suited for incorporation with deep convolutional networks. We evaluate
our model on benchmark segmentation datasets in skin cancer segmentation and lung
lesion segmentation. Results show highly competitive performance when compared with U-
Net and it's residual variant.
We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder. Our attention architecture is well suited for incorporation with deep convolutional networks. We evaluate our model on benchmark segmentation datasets in skin cancer segmentation and lung lesion segmentation. Results show highly competitive performance when compared with U-Net and it's residual variant.
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