  This paper proposes an efficient solution for tumor segmentation and
classification in breast ultrasound (BUS) images. We propose to add an atrous
convolution layer to the conditional generative adversarial network (cGAN)
segmentation model to learn tumor features at different resolutions of BUS
images. To automatically re-balance the relative impact of each of the highest
level encoded features, we also propose to add a channel-wise weighting block
in the network. In addition, the SSIM and L1-norm loss with the typical
adversarial loss are used as a loss function to train the model. Our model
outperforms the state-of-the-art segmentation models in terms of the Dice and
IoU metrics, achieving top scores of 93.76% and 88.82%, respectively. In the
classification stage, we show that few statistics features extracted from the
shape of the boundaries of the predicted masks can properly discriminate
between benign and malignant tumors with an accuracy of 85%$
