Gen AI for Medical Imaging: How SALAD Brings Spatially Aware Lesion Synthesis to Pathological Data

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@article{1724917,
      author = {Dilip Sharma},
      title = {Gen AI for Medical Imaging: How SALAD Brings Spatially Aware Lesion Synthesis to Pathological Data},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {6},
      pages = {475-480},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724917.pdf},
      abstract = {Medical imaging datasets suffer from the problem of scarcity of pathological examples especially rare diseases. The performance of deep learning models in diagnostic applications is significantly limited by this data imbalance. This paper presents SALAD, a generative AI framework that aims to create pathological images with spatially aware lesion augmentation and diffusion. Unlike general generation models, SALAD integrates four customized architectural elements dedicated to medical imaging. A multiscale feature encoder identifies lesions measuring from one millimetre to thirty millimetre in diameter. An attention mechanism that focuses on lesions actively centres computation on pathology. This paper proposes an adaptive noise schedule that learns optimal denoising kinetics for the medical image domain. Lastly, a background preservation module ensures sufficient fidelity of the surrounding healthy anatomy by restricting the manipulation to the lesion. The comprehensive evaluation on two benchmark medical imaging datasets demonstrates that SALAD achieves 89% DICE score. The model only needs fifty denoising iterations rather than the one thousand steps of regular diffusion models, offering a twenty fold speed up. Additionally, segmentation models that are trained on augmented datasets generated by SALAD improve performance by almost six percent. Our results indicate that generative models for medical images would require domain-specific design principles, as diagnostic accuracy rather than visual accuracy is the primary goal.},
      month = {December},
      doi = {https://doi.org/10.64388/ire1724917}
  }