A5013862806- Cancer-related molecular mechanisms research
- Multimodal Machine Learning Applications
- AI in cancer detection
- Generative Adversarial Networks and Image Synthesis
- Natural Language Processing Techniques
Yantai University
2022-2024
Medical image-to-image translation is considered a new direction with many potential applications in the medical field. The dominated by two models, including supervised Pix2Pix and unsupervised cyclic-consistency generative adversarial network (GAN). However, existing methods still have shortcomings: 1) requires paired pixel-aligned images, which are difficult to acquire. Nevertheless, optimum output of cycle-consistency model may not be unique. 2) They deficient capturing global features...
Existing magnetic resonance imaging translation models rely on generative adversarial networks, primarily employing simple convolutional neural networks. Unfortunately, these networks struggle to capture global representations and contextual relationships within images. While the advent of Transformers enables capturing long-range feature dependencies, they often compromise preservation local details. To address limitations enhance both representations, we introduce DBGAN , a novel...