A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT
2026-06-15 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionMachine Learning
AI summaryⓘ
The authors created a new large dataset and benchmark to help computers learn how to spot abdominal diseases and write reports using non-contrast CT scans instead of the usual contrast-enhanced scans, which can have risks and higher workload. They collected paired data from two centers and tested several AI models to see how well they could predict disease without using contrast agents. Their experiments showed that non-contrast CT images still contain useful information for diagnosis across multiple organs. By sharing their data and tools, the authors hope to encourage research into safer and more efficient abdominal imaging methods that do not rely on contrast enhancement.
Computed Tomography (CT)Contrast-Enhanced CT (CECT)Non-Contrast CT (NCCT)Abdominal ImagingRadiology Report GenerationDeep LearningMulti-Organ DiagnosisBenchmark DatasetContrast-Induced NephropathyMulti-Center Study
Authors
Mariam Elbakry, Aliaa Sayed Sheha, Salma Hassan Tantawy, Aya Yassin, Concetto Spampinato, Karim Lekadir, Xiaomeng Li, Marawan Elbatel
Abstract
Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition burden, and heavily contributes to radiologist workload. To address these challenges, we introduce a novel multi-center benchmark for multi-organ abdominal disease diagnosis and automated radiology report generation, which learns to synthesize contrast-enhanced findings from single-phase non-contrast CT (NCCT). To support this, we curated a large-scale dataset of paired NCCT-CECT studies and their corresponding contrast-enhanced radiology reports from two centers, partitioned into internal sets and an external validation cohort. Under a unified evaluation protocol, we benchmarked five contemporary deep learning architectures encompassing chest-specific, abdomen-specific, and general-purpose multimodal domains. Extensive experiments demonstrate that NCCT retains diagnostic signals, achieving an average multi-organ AUC of 69.1% on the internal cohort and 63.1% on the external cohort, respectively. By releasing this dataset and standardized benchmark publicly, this study aims to catalyze future research into safer, resource-efficient, and globally accessible contrast-free abdominal imaging workflows. Code is available at: https://github.com/xmed-lab/TriALS-Report.