PMPBench: A Paired Multi-Modal Pan-Cancer Benchmark for Medical Image Synthesis

• URL: http://arxiv.org/abs/2601.15884v1
• Date: Thu, 22 Jan 2026 11:58:37 GMT
• Title: PMPBench: A Paired Multi-Modal Pan-Cancer Benchmark for Medical Image Synthesis
• Authors: Yifan Chen, Fei Yin, Hao Chen, Jia Wu, Chao Li,
• Abstract summary: We release the first public, fully paired, pan-cancer medical imaging dataset spanning 11 human organs.<n>The dataset is curated for anatomical correspondence, enabling rigorous evaluation of translation settings.<n>We report results from representative baselines of contemporary image-to-image translation.
• Score: 33.41070177089698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrast medium plays a pivotal role in radiological imaging, as it amplifies lesion conspicuity and improves detection for the diagnosis of tumor-related diseases. However, depending on the patient's health condition or the medical resources available, the use of contrast medium is not always feasible. Recent work has explored AI-based image translation to synthesize contrast-enhanced images directly from non-contrast scans, aims to reduce side effects and streamlines clinical workflows. Progress in this direction has been constrained by data limitations: (1) existing public datasets focus almost exclusively on brain-related paired MR modalities; (2) other collections include partially paired data but suffer from missing modalities/timestamps and imperfect spatial alignment; (3) explicit labeling of CT vs. CTC or DCE phases is often absent; (4) substantial resources remain private. To bridge this gap, we introduce the first public, fully paired, pan-cancer medical imaging dataset spanning 11 human organs. The MR data include complete dynamic contrast-enhanced (DCE) sequences covering all three phases (DCE1-DCE3), while the CT data provide paired non-contrast and contrast-enhanced acquisitions (CTC). The dataset is curated for anatomical correspondence, enabling rigorous evaluation of 1-to-1, N-to-1, and N-to-N translation settings (e.g., predicting DCE phases from non-contrast inputs). Built upon this resource, we establish a comprehensive benchmark. We report results from representative baselines of contemporary image-to-image translation. We release the dataset and benchmark to catalyze research on safe, effective contrast synthesis, with direct relevance to multi-organ oncology imaging workflows. Our code and dataset are publicly available at https://github.com/YifanChen02/PMPBench.

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