Medical Image Synthesis via Fine-Grained Image-Text Alignment and Anatomy-Pathology Prompting
<|reference_start|>Medical Image Synthesis via Fine-Grained Image-Text Alignment and Anatomy-Pathology Prompting: Data scarcity and privacy concerns limit the availability of high-quality medical images for public use, which can be mitigated through medical image synthesis. However, current medical image synthesis methods often struggle to accurately capture the complexity of detailed anatomical structures and pathological conditions. To address these challenges, we propose a novel medical image synthesis model that leverages fine-grained image-text alignment and anatomy-pathology prompts to generate highly detailed and accurate synthetic medical images. Our method integrates advanced natural language processing techniques with image generative modeling, enabling precise alignment between descriptive text prompts and the synthesized images' anatomical and pathological details. The proposed approach consists of two key components: an anatomy-pathology prompting module and a fine-grained alignment-based synthesis module. The anatomy-pathology prompting module automatically generates descriptive prompts for high-quality medical images. To further synthesize high-quality medical images from the generated prompts, the fine-grained alignment-based synthesis module pre-defines a visual codebook for the radiology dataset and performs fine-grained alignment between the codebook and generated prompts to obtain key patches as visual clues, facilitating accurate image synthesis. We validate the superiority of our method through experiments on public chest X-ray datasets and demonstrate that our synthetic images preserve accurate semantic information, making them valuable for various medical applications.<|reference_end|>
@article{chen2024medical,
    title={Medical Image Synthesis via Fine-Grained Image-Text Alignment and
  Anatomy-Pathology Prompting},
    author={Wenting Chen, Pengyu Wang, Hui Ren, Lichao Sun, Quanzheng Li, Yixuan
  Yuan, and Xiang Li},
    journal={arXiv preprint arXiv:2403.06835},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06835},
    primaryClass={cs.CV cs.AI cs.CL}
}