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

An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning