Jul 7, 2019 · Abstract page for arXiv paper 1907.03297: Dual Adversarial Learning with Attention Mechanism for Fine-grained Medical Image Synthesis.
The deep reason behind is that the training of the network tends to be dominated by samples/regions that are easy to synthesize, i.e., normal tissue regions.
Jul 24, 2024 · We propose a simple yet effective strategy: a dual-discriminator (dual-D) adversarial learning system.
Experimental results show the robustness and accuracy of our method in synthesizing fine-grained target images from the corresponding source images. In ...
Dual Adversarial Learning with Attention Mechanism for Fine-grained Medical Image Synthesis. Resource URI: https://dblp.l3s.de/d2r/resource/publications ...
Fine-Grained Medical Image Synthesis with Dual-Attention Adversarial Learning ... Nie, D., et al.: Medical image synthesis with context-aware generative ...
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Dual Adversarial Learning with Attention Mechanism for Fine-grained Medical Image Synthesis ... Synthesizing CT Image from T1-Weighted MR Image · no code ...
Oct 22, 2024 · Generative adversarial networks (GAN) are widely used in medical image analysis tasks, such as medical image segmentation and synthesis.
In this paper, we propose a novel end-to-end framework GCNet for automated Glaucoma Classification based on ACA images or other Glaucoma-related medical images.
Deep Learning Attention Mechanism in Medical Image Analysis
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Mar 27, 2023 · This paper reviews the deep learning attention methods in medical image analysis. A comprehensive literature survey is first conducted to analyze the keywords ...