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ReXGradient-160K: A Large-Scale Publicly Available Dataset of Chest Radiographs with Free-text Reports
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ReXGradient-160K: A Large-Scale Publicly Available Dataset of Chest Radiographs with Free-text Reports
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We present ReXGradient-160K, representing the largest publicly available chest X-ray dataset to date in terms of the number of patients. This dataset contains 160,000 chest X-ray studies with paired radiological reports from 109,487 unique patients across 3 U.S. health systems (79 medical sites). This comprehensive dataset includes multiple images per study and detailed radiology reports, making it particularly valuable for the development and evaluation of AI systems for medical imaging and automated report generation models. The dataset is divided into training (140,000 studies), validation (10,000 studies), and public test (10,000 studies) sets, with an additional private test set (10,000 studies) reserved for model evaluation on the ReXrank benchmark. By providing this extensive dataset, we aim to accelerate research in medical imaging AI and advance the state-of-the-art in automated radiological analysis. Our dataset will be open-sourced at https://huggingface.co/datasets/rajpurkarlab/ReXGradient-160K.
Forward citations
Cited by 13 Pith papers
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ECHO is a one-step block diffusion VLM for chest X-ray reports that improves RaTE and SemScore by over 60% while delivering 8x faster inference than autoregressive baselines.
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Temporal Inversion for Learning Interval Change in Chest X-Rays
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RadHarmony: Radiological Data Handling in the Era of Agentic AI
RadHarmony is a unified Python library and AI-agent workflow for harmonizing 24 public radiology datasets, demonstrated by a multi-dataset self-supervised chest X-ray model with no dataset-specific code.
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Prospective clinical indication, post-hoc report leakage, and fusion design in multi-image chest radiograph classification: a patient-clustered evaluation
Report-derived chest X-ray labels are almost perfectly predicted by the report text itself (AUROC 0.98), while images plus prospective indication reach only AUROC 0.78, quantifying report-label circularity.
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SDR: Set-Distance Rewards for Radiology Report Generation
Set-to-set distances on sentence embeddings provide a permutation-invariant reward signal that improves GRPO training and enables efficient test-time scaling for vision-language models generating chest X-ray reports.
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SemEnrich enriches radiology reports with positive/neutral findings via self-supervised semantic clustering, yielding average gains of 5-7% on COMET, BERT score, Sentence BLEU, CheXbert-F1 and RadGraph-F1 after fine-t...
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UniRG-CXR, a Qwen3-VL-8B model trained with SFT plus GRPO reinforcement learning that directly optimizes the ReXrank metric components, reports state-of-the-art 1/RadCliQ-v1 results on all four ReXrank chest X-ray dat...
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