Kidney and tumor segmentation using an ensemble of deep neural networks

For the segmentation of kidney and tumor task, we propose a two stages model that consists of several classification networks and segmentation models. The first stage is the foreground and background classification subnetwork, this stage is to recognize whether there are kidneys or tumors on images, so we propose a classification model called RD-Net which can effectively reduce the errors caused by a large of background images and improve the efficiency of the whole segmentation results. The second stage is the segmentation model used to predict the contour of the target (kidney or tumor). Therefore, we propose Att-ResUnet model and multi-scale ensemble of postprocessing methods used to integrate the predicted results of multiple models, so as to improve the accuracy of prediction results.