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.
|Title of host publication
|International Symposium on Biomedical Imaging (ISBI)
|Place of Publication
|Published - Apr 2019
- Semantic segmentation, attention in CNNs, medical imaging, U-Net, residual connections