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Background: Gadolinium‐based contrast agents are commonly used in brain magnetic resonance imaging (MRI), however, they cannot be used by patients with allergic reactions or poor renal function. For long‐term follow‐up patients, gadolinium deposition in the body can cause nephrogenic systemic fibrosis and other potential risks. Purpose: Developing a new method of enhanced image synthesis based on the advantages of multisequence MRI has important clinical value for these patients. In this paper, an end‐to‐end synthesis model structure similarity index measure (SSIM)‐based Dual Constrastive Learning with Attention (SDACL) based on contrastive learning is proposed to synthesize contrast‐enhanced T1 (T1ce) using three unenhanced MRI images of T1, T2, and Flair in patients with glioma. Methods: The model uses the attention–dilation generator to enlarge the receptive field by expanding the residual blocks and to strengthen the feature representation and context learning of multisequence MRI. To enhance the detail and texture performance of the imaged tumor area, a comprehensive loss function combining patch‐level contrast loss and structural similarity loss is created, which can effectively suppress noise and ensure the consistency of synthesized images and real images. Results: The normalized root‐mean‐square error (NRMSE), peak signal‐to‐noise ratio (PSNR), and SSIM of the model on the independent test set are 0.307 ±$\pm$ 0.12, 23.337 ±$\pm$ 3.21, and 0.881 ±$\pm$ 0.05, respectively. Conclusions: Results show this method can be used for the multisequence synthesis of T1ce images, which can provide valuable information for clinical diagnosis. [ABSTRACT FROM AUTHOR]

Purpose: Breast mass segmentation is a prerequisite step in the use of computer‐aided tools designed for breast cancer diagnosis and treatment planning. However, mass segmentation remains challenging due to the low contrast, irregular shapes, and fuzzy boundaries of masses. In this work, we propose a mammography mass segmentation model for improving segmentation performance. Methods: We propose a mammography mass segmentation model called SAP‐cGAN, which is based on an improved conditional generative adversarial network (cGAN). We introduce a superpixel average pooling layer into the cGAN decoder, which utilizes superpixels as a pooling layout to improve boundary segmentation. In addition, we adopt a multiscale input strategy to enable the network to learn scale‐invariant features with increased robustness. The performance of the model is evaluated with two public datasets: CBIS‐DDSM and INbreast. Moreover, ablation analysis is conducted to evaluate further the individual contribution of each block to the performance of the network. Results: Dice and Jaccard scores of 93.37% and 87.57%, respectively, are obtained for the CBIS‐DDSM dataset. The Dice and Jaccard scores for the INbreast dataset are 91.54% and 84.40%, respectively. These results indicate that our proposed model outperforms current state‐of‐the‐art breast mass segmentation methods. The superpixel average pooling layer and multiscale input strategy has improved the Dice and Jaccard scores of the original cGAN by 7.8% and 12.79%, respectively. Conclusions: Adversarial learning with the addition of a superpixel average pooling layer and multiscale input strategy can encourage the Generator network to generate masks with increased realism and improve breast mass segmentation performance through the minimax game between the Generator network and Discriminator network. [ABSTRACT FROM AUTHOR]