The reviewed record of science sign in
Pith

arxiv: 2505.00228 · v2 · pith:NBE7FLHW · submitted 2025-05-01 · eess.IV · cs.CV

ReXGradient-160K: A Large-Scale Publicly Available Dataset of Chest Radiographs with Free-text Reports

Xiaoman Zhang , Juli\'an N. Acosta , Josh Miller , Ouwen Huang , Pranav Rajpurkar This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:NBE7FLHWrecord.jsonopen to challenge →

classification eess.IV cs.CV
keywords datasetstudieschestmedicalreportsrexgradient-160kautomatedavailable
0
0 comments X
read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. CheXTemporal: A Dataset for Temporally-Grounded Reasoning in Chest Radiography

    cs.CV 2026-05 accept novelty 8.0

    CheXTemporal supplies paired chest X-rays with explicit temporal progression taxonomy and spatial grounding to benchmark and improve models on longitudinal reasoning tasks.

  2. Transition-Aware best-of-N sampling for Longitudinal Chest X-ray Reports

    cs.CV 2026-06 unverdicted novelty 7.0

    Transition-aware best-of-N sampling embeds report sentences as sets, computes directional transition vectors via set-to-set distances, and scores candidates by proximity to ground-truth training transitions.

  3. Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers

    cs.CV 2026-06 unverdicted novelty 7.0

    A 1.3B-parameter rectified flow transformer is the first generative foundation model for chest radiograph synthesis at billion-parameter scale, producing images indistinguishable from real ones to experts.

  4. CXR-ContraBench: Benchmarking Negated-Option Attraction in Medical VLMs

    cs.CV 2026-05 conditional novelty 7.0

    Medical VLMs frequently select negated options that contradict visible chest X-ray findings, achieving only ~30% accuracy on direct presence probes, but a post-hoc consistency verifier raises accuracy above 95%.

  5. ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion

    cs.LG 2026-04 unverdicted novelty 7.0

    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.

  6. Temporal Inversion for Learning Interval Change in Chest X-Rays

    cs.CV 2026-04 unverdicted novelty 7.0

    TILA uses temporal inversion of image pairs as a supervisory signal to make existing temporal vision-language models more sensitive to directional interval changes in chest X-rays.

  7. SDR: Set-Distance Rewards for Radiology Report Generation

    cs.AI 2026-05 unverdicted novelty 6.0

    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.

  8. SemEnrich: Self-Supervised Semantic Enrichment of Radiology Reports for Vision-Language Learning

    cs.LG 2026-04 unverdicted novelty 6.0

    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...

  9. Harrison.Rad 1.5 Technical Report: A radiology foundation model that can draft reports from images, priors and clinical context

    cs.CV 2026-07 conditional novelty 5.0

    Harrison.Rad 1.5 is a radiology-specific multimodal LLM that passes simulated FRCR 2B Short Case examinations and outperforms general-purpose frontier models on plain-film radiography reporting tasks.

  10. ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion

    cs.LG 2026-04 unverdicted novelty 5.0

    ECHO introduces one-step block diffusion via Direct Conditional Distillation and Response-Asymmetric Diffusion to generate chest X-ray reports faster than autoregressive models while improving clinical metrics.