RadiomicsFill-Mammo: Synthetic Mammogram Mass Manipulation with Radiomics Features
<|reference_start|>RadiomicsFill-Mammo: Synthetic Mammogram Mass Manipulation with Radiomics Features: Motivated by the question, "Can we generate tumors with desired attributes?'' this study leverages radiomics features to explore the feasibility of generating synthetic tumor images. Characterized by its low-dimensional yet biologically meaningful markers, radiomics bridges the gap between complex medical imaging data and actionable clinical insights. We present RadiomicsFill-Mammo, the first of the RadiomicsFill series, an innovative technique that generates realistic mammogram mass images mirroring specific radiomics attributes using masked images and opposite breast images, leveraging a recent stable diffusion model. This approach also allows for the incorporation of essential clinical variables, such as BI-RADS and breast density, alongside radiomics features as conditions for mass generation. Results indicate that RadiomicsFill-Mammo effectively generates diverse and realistic tumor images based on various radiomics conditions. Results also demonstrate a significant improvement in mass detection capabilities, leveraging RadiomicsFill-Mammo as a strategy to generate simulated samples. Furthermore, RadiomicsFill-Mammo not only advances medical imaging research but also opens new avenues for enhancing treatment planning and tumor simulation. Our code is available at https://github.com/nainye/RadiomicsFill.<|reference_end|>
@article{na2024radiomicsfill-mammo:,
    title={RadiomicsFill-Mammo: Synthetic Mammogram Mass Manipulation with
  Radiomics Features},
    author={Inye Na, Jonghun Kim, Eun Sook Ko, Hyunjin Park},
    journal={arXiv preprint arXiv:2407.05683},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05683},
    primaryClass={eess.IV cs.AI cs.CV}
}