RadiomicsFill-Mammo: Synthetic Mammogram Mass Manipulation with Radiomics Features
- URL: http://arxiv.org/abs/2407.05683v2
- Date: Sun, 29 Sep 2024 08:58:47 GMT
- Title: RadiomicsFill-Mammo: Synthetic Mammogram Mass Manipulation with Radiomics Features
- Authors: Inye Na, Jonghun Kim, Eun Sook Ko, Hyunjin Park,
- Abstract summary: We present RadiomicsFill-Mammo, an innovative technique that generates realistic mammogram mass images mirroring specific radiomics attributes.
Results indicate that RadiomicsFill-Mammo effectively generates diverse and realistic tumor images based on various radiomics conditions.
- Score: 3.0015555136149175
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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