Medical image segmentation using deep neural networks has been highly
successful. However, the effectiveness of these networks is often limited by
inadequate dense prediction and inability to extract robust features. To
achieve refined dense prediction, we propose densely decoded networks (ddn), by
selectively introducing 'crutch' network connections. Such 'crutch' connections
in each upsampling stage of the network decoder (1) enhance target localization
by incorporating high resolution features from the encoder, and (2) improve
segmentation by facilitating multi-stage contextual information flow. Further,
we present a training strategy based on adaptive deep supervision (ads), which
exploits and adapts specific attributes of input dataset, for robust feature
extraction. In particular, ads strategically locates and deploys auxiliary
supervision, by matching the average input object size with the layer-wise
effective receptive fields (lerf) of a network, resulting in a class of ddns.
Such inclusion of 'companion objective' from a specific hidden layer, helps the
model pay close attention to some distinct input-dependent features, which the
network might otherwise 'ignore' during training. Our new networks and training
strategy are validated on 4 diverse datasets of different modalities,
demonstrating their effectiveness.

提出了一种基于深度神经网络的医学图像分割方法，通过引入 ' 拐杖 ' 网络连接以获得精细的密集预测，并采用自适应深度监督训练策略以提取鲁棒特征。通过在网络解码器的每个上采样阶段引入 ' 拐杖 ' 连接，增强了目标定位和分割性能，并通过匹配网络的有效感受野来指导辅助监督位置的选择，从而实现了对特定输入特征的注意。在 4 个不同模态的多样数据集上验证了该方法的有效性。