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- W4385557258 abstract "Image synthesis methods based on deep learning has recently achieved success in reducing the dosage of gadolinium-based contrast agents (GBCAs). However, these methods cannot focus on the region of interest to synthesize realistic images. To address this issue, a mask guided attention generative adversarial network (MGA-GAN) was proposed to synthesize contrast enhanced T1-weight images from the multi-channel inputs. Qualitive and quantitative results indicate that the proposed MGA-GAN can improve the synthesized images with higher quality for details of brainstem glioma, compared with state-of-the-art methods." @default.
- W4385557258 created "2023-08-04" @default.
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- W4385557258 date "2023-08-03" @default.
- W4385557258 modified "2023-09-27" @default.
- W4385557258 title "A Mask Guided Attention Generative Adversarial Network for Contrast-enhanced T1-weight MR Synthesis" @default.
- W4385557258 doi "https://doi.org/10.58530/2022/1968" @default.
- W4385557258 hasPublicationYear "2023" @default.
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