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.

翻译：我们提出一种新的方法,将注意力纳入进进化神经网络中,使用一个单独的进化自动编码器生成的地貌图。我们的注意力结构非常适合与深进化网络结合。我们评估了皮肤癌分解和肺损伤分解的基准分解数据集模型。结果显示,与U-Net及其剩余变异相比,其性能极具竞争力。