DREAM: Error-Guided Learning for Robust Cancer Detection

Segmentation models can surpass radiologists at detecting cancer on computed tomography (CT). However, models that perform well on standard datasets often degrade in real-world deployment, by producing more false negatives (missed tumors) and more false positives (false alarms). These errors concentrate on hard cases. Hard negatives are often non-cancer abnormalities that trigger false alarms. Hard positives are small early tumors that are often missed. To address these systematic errors, we propose Diagnostic Robustness by Error Analysis and Modeling (DREAM). The framework has three components. (1) Report-based mining: LLMs read 440,000 radiology reports to find hard negatives and hard positives. The corresponding CT scans are added to training, so the segmentation model learns from the cases it most often gets wrong. (2) Report-refined masks: an algorithm refines AI-generated tumor masks to match the tumor size, count, and location described in the radiology report. This avoids time-consuming manual annotation. (3) A cascade verifier combines the tumor segmentation model's output with a radiomics-based classifier of non-cancerous abnormalities to improve the tumor probability estimate and reduce the risk of mistaking other abnormalities for cancer. We evaluated DREAM on the detection of tumors in the pancreas, bladder, and gallbladder. DREAM consistently outperforms 13 state-of-the-art methods and public AI models on internal and external validation. Gains over the nnU-Net baseline reach up to +6.7 pp in AUC on tumor detection, and DREAM improves specificity by up to +40.7 pp on non-cancer abnormalities. In a reader study, DREAM detected 2.4 times more early pancreatic tumors than the average of three radiologists at matched specificity. These results show that AI errors cluster into systematic, identifiable patterns. Targeted mining, report-refined masks, and abnormality-aware verification are effective strategies to reduce these errors. Code and models will be publicly available.