T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images

2025/07/05 by Christopher Wiedeman, Wiedeman, Christopher, Anastasiia Sarmakeeva +11
Computer Science · Medicine · #AI in cancer detection #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Digital Radiography and Breast Imaging #FOS: Computer and information sciences #Medical Imaging Techniques and Applications

paper · pdf · doi:10.48550/arxiv.2507.04038

openalex publication_date 2025/07/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

One of the key impediments for developing and assessing robust medical imaging algorithms is limited access to large-scale datasets with suitable annotations. Synthetic data generated with plausible physical and biological constraints may address some of these data limitations. We propose the use of physics simulations to generate synthetic images with pixel-level segmentation annotations, which are notoriously difficult to obtain. Specifically, we apply this approach to breast imaging analysis and release T-SYNTH, a large-scale open-source dataset of paired 2D digital mammography (DM) and 3D digital breast tomosynthesis (DBT) images. Our initial experimental results indicate that T-SYNTH images show promise for augmenting limited real patient datasets for detection tasks in DM and DBT. Our data and code are publicly available at https://github.com/DIDSR/tsynth-release.

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• Robust breast cancer detection in mammography and digital breast\n tomosynthesis using annotation-efficient deep learning approach