Title: CheXGenBench: A Unified Benchmark for Fidelity, Privacy and Utility of Synthetic Chest Radiographs
Primary area: datasets and benchmarks
Keywords: Biomedical Imaging, Text-to-Image Generation, Medical Image Analysis, Chest Radiographs, Benchmark

Abstract: Structured benchmarks have advanced text-conditional image generation for real-world imagery, however, no such benchmark exists for synthetic radiograph generation. Despite being a highly active area of research, existing studies continue adopting inconsistent evaluation protocols and lack a unified assessment of the three most critical criteria: generative fidelity, privacy risk, and downstream utility. 
To address these limitations, we introduce CheXGenBench, the first unified evaluation framework for synthetic chest radiograph generation that simultaneously assesses fidelity, privacy risks, and clinical utility across frontier text-to-image (T2I) generative models. Our evaluation protocol, comprising over 20 quantitative metrics, covers 11 leading T2I architectures with plug-and-play integration for newer models. Through a rigorous and fair evaluation protocol, we establish a new SoTA in synthetic chest X-ray generation. Furthermore, our results uncover several critical limitations in the applicability of current generative models, which include (1) even SoTA models struggle with long-tailed medical distributions, (2) models pose high privacy risks regardless of fidelity quality, and (3) synthetic data offers limited utility for downstream multimodal tasks. Drawing from these results, we propose concrete research directions to advance the field.
Finally, we curate and release SynthCheX-75K, a high-quality synthetic dataset comprising 75K radiographs generated by our top-performing model (Sana 0.6B).
The fine-tuned models and the SynthCheX-75K dataset would be released after acceptance, while the anonymised code is available at https://anonymous.4open.science/r/CheXGenBench-52F0/README.md

TL;DR: We introduce CheXGenBench, a rigorous and multifaceted evaluation framework for synthetic chest radiograph generation that simultaneously assesses fidelity, privacy risks, and clinical utility across state-of-the-art text-to-image generative models