Efficient binary cnn for medical image segmentation

In this work, we propose accurate binary Depthwise Separable Convolutional Neural Networks (DSCNNs) for medical image segmentation. The networks are binarized by learning the distribution of weights and activations, and by using parameter-free skip connections in their encoder and decoder structure. We design full precision DSCNNs based on a symmetric encoder-decoder, feature pyramid network with an asymmetric decoder, and spatial pyramid pooling with atrous convolutions strategies for image segmentation. The DSCNNs have 14 X and 8 X fewer number of model parameters and operations, respectively, than standard segmentation networks. The trained full precision DSCNNs are used as baselines to achieve accurate binary DSCNNs. The networks are trained on two medical ultrasound datasets, a public fetal skull dataset and a privileged bladder dataset. The accuracy of the binary DSCNNs are within a 3% drop from the full precision networks on both the medical datasets.