FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation
This work addresses segmentation accuracy in medical imaging, but it is incremental as it builds on existing attention and convolutional methods.
The authors tackled medical image segmentation by incorporating attention within convolutional neural networks using a separate convolutional autoencoder, achieving highly competitive performance compared to U-Net and its residual variant on skin cancer and lung lesion datasets.
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.