mlsanityData to 14 Oct 2026

HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis

arXiv 2405.04902 ↗First posted 8 May 2024eess.IV cs.CVcited by 2

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Abstract (arXiv)

Medical Image Synthesis (MIS) plays an important role in the intelligent medical field, which greatly saves the economic and time costs of medical diagnosis. However, due to the complexity of medical images and similar characteristics of different tissue cells, existing methods face great challenges in meeting their biological consistency. To this end, we propose the Hybrid Augmented Generative Adversarial Network (HAGAN) to maintain the authenticity of structural texture and tissue cells. HAGAN contains Attention Mixed (AttnMix) Generator, Hierarchical Discriminator and Reverse Skip Connection between Discriminator and Generator. The AttnMix consistency differentiable regularization encourages the perception in structural and textural variations between real and fake images, which improves the pathological integrity of synthetic images and the accuracy of features in local areas. The Hierarchical Discriminator introduces pixel-by-pixel discriminant feedback to generator for enhancing the saliency and discriminance of global and local details simultaneously. The Reverse Skip Connection further improves the accuracy for fine details by fusing real and synthetic distribution features. Our experimental evaluations on three datasets of different scales, i.e., COVID-CT, ACDC and BraTS2018, demonstrate that HAGAN outperforms the existing methods and achieves state-of-the-art performance in both high-resolution and low-resolution.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 39 of its 70 references that are in the map. First posted on arXiv 8 May 2024.

Builds on

39 of 70 references.

References · 39

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27 more
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Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMscited by 23
  2. 20263D-LLDM: Label-Guided 3D Latent Diffusion Model for Improving High-Resolution Synthetic MR Imaging in Hepatic Structure Segmentationcited by 2