CheXTemporal supplies paired chest X-rays with explicit temporal progression taxonomy and spatial grounding to benchmark and improve models on longitudinal reasoning tasks.
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Chexpert plus: Hundreds of thousands of aligned radiology texts, im- ages and patients
19 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
19
Pith papers citing it
5
external citations · Pith
abstract
Since the release of the original CheXpert paper five years ago, CheXpert has become one of the most widely used and cited clinical AI datasets. The emergence of vision language models has sparked an increase in demands for sharing reports linked to CheXpert images, along with a growing interest among AI fairness researchers in obtaining demographic data. To address this, CheXpert Plus serves as a new collection of radiology data sources, made publicly available to enhance the scaling, performance, robustness, and fairness of models for all subsequent machine learning tasks in the field of radiology. CheXpert Plus is the largest text dataset publicly released in radiology, with a total of 36 million text tokens, including 13 million impression tokens. To the best of our knowledge, it represents the largest text de-identification effort in radiology, with almost 1 million PHI spans anonymized. It is only the second time that a large-scale English paired dataset has been released in radiology, thereby enabling, for the first time, cross-institution training at scale. All reports are paired with high-quality images in DICOM format, along with numerous image and patient metadata covering various clinical and socio-economic groups, as well as many pathology labels and RadGraph annotations. We hope this dataset will boost research for AI models that can further assist radiologists and help improve medical care. Data is available at the following URL: https://stanfordaimi.azurewebsites.net/datasets/5158c524-d3ab-4e02-96e9-6ee9efc110a1 Models are available at the following URL: https://github.com/Stanford-AIMI/chexpert-plus
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background 2representative citing papers
LePaX enables high-resolution chest X-ray report generation by learning to allocate resolution to diagnostically relevant regions and fusing high-res patches back into global features without increasing token count.
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.
Introduces MedReCo-DB dataset of 690k+ images and entity-aware models MedReCo/MedReCo-VLM that improve reference retrieval and comparative change interpretation in radiology across multiple centers and modalities.
AutoMedBench evaluates AI agents on long-horizon medical workflows across five stages and finds validation and submission as dominant failure points based on thousands of runs.
CheXthought supplies large-scale expert chain-of-thought reasoning and synchronized visual attention data for chest X-rays to train more accurate and interpretable clinical vision-language models.
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.
Cached structured report annotations let radiology label schemas be changed with dictionary edits instead of relabeling the corpus, recovering long-tail findings at near-zero marginal cost.
DIVE improves in-context vector distillation for medical report generation via decisive-token supervision on pathology terms and EOS plus state-conditioned dynamic steering, achieving top BLEU-4, ROUGE-L and RadGraph F1 on MIMIC-CXR and CheXpert Plus.
Empirical study finds overconfidence persists in medical VLMs despite scaling and prompting; post-hoc calibration reduces error while hallucination-aware calibration improves both calibration and AUROC.
CARPA generates anatomically faithful synthetic chest X-rays with controlled clinical concept insertions and deletions to expand training coverage and improve model precision, calibration, and reliability on real benchmarks.
MedBridge adapts pretrained VLMs to multi-label medical diagnosis via query tokens for non-destructive alignment and expert routing, reporting 6-15% AUC gains on chest radiograph benchmarks across eight models.
RA-RRG extracts key phrases with LLMs, retrieves them via multimodal similarity, and conditions report generation on them to achieve SOTA CheXbert scores and competitive RadGraph F1 on MIMIC-CXR and IU X-ray while supporting multi-view inputs.
Lightweight metrics trained on Qwen3-8B and MedGemma-4B using synthetic pairs outperform larger medical LLMs at distinguishing clinical significance in radiology reports while balancing discrimination and robustness.
A supervision construction procedure generates explicit support and controlled non-support examples (counterfactual and topic-related negatives) without manual annotation, producing verifiers that demonstrate genuine evidence dependence in radiology tasks.
Deep vision models predict health insurance type from normal chest X-rays at AUC ~0.70, indicating capture of socioeconomic signals beyond demographics.
RadAgents is a multi-agent framework coupling clinical priors with task-aware multimodal reasoning and radiologist-like workflows, plus grounding and retrieval-augmentation for conflict resolution in chest X-ray interpretation.
MedXIAOHE is a medical MLLM that claims state-of-the-art benchmark performance through specialized pretraining to cover long-tail diseases and RL-based reasoning training.
The paper describes Baichuan-M4, a coordinated medical agent system that reports leading scores across static knowledge, dynamic consultation, long-context memory, retrieval, OCR, and multimodal tasks with a 3.3% hallucination rate.
citing papers explorer
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CheXTemporal: A Dataset for Temporally-Grounded Reasoning in Chest Radiography
CheXTemporal supplies paired chest X-rays with explicit temporal progression taxonomy and spatial grounding to benchmark and improve models on longitudinal reasoning tasks.
-
Seeing What Matters: Lesion-Aware High-Resolution Patch Discovery and Fusion for Chest X-ray Report Generation
LePaX enables high-resolution chest X-ray report generation by learning to allocate resolution to diagnostically relevant regions and fusing high-res patches back into global features without increasing token count.
-
Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers
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.
-
A Vision-language Framework for Comparative Reasoning in Radiology
Introduces MedReCo-DB dataset of 690k+ images and entity-aware models MedReCo/MedReCo-VLM that improve reference retrieval and comparative change interpretation in radiology across multiple centers and modalities.
-
AutoMedBench: Towards Medical AutoResearch with Agentic AI Models
AutoMedBench evaluates AI agents on long-horizon medical workflows across five stages and finds validation and submission as dominant failure points based on thousands of runs.
-
CheXthought: A global multimodal dataset of clinical chain-of-thought reasoning and visual attention for chest X-ray interpretation
CheXthought supplies large-scale expert chain-of-thought reasoning and synchronized visual attention data for chest X-rays to train more accurate and interpretable clinical vision-language models.
-
Temporal Inversion for Learning Interval Change in Chest X-Rays
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.
-
Reconfigurable Radiology Labels Without Relabeling
Cached structured report annotations let radiology label schemas be changed with dictionary edits instead of relabeling the corpus, recovering long-tail findings at near-zero marginal cost.
-
Not All Tokens Matter Equally: Dynamic In-context Vector Distillation with Decisive-Token Supervision for Long-form Medical Report Generation
DIVE improves in-context vector distillation for medical report generation via decisive-token supervision on pathology terms and EOS plus state-conditioned dynamic steering, achieving top BLEU-4, ROUGE-L and RadGraph F1 on MIMIC-CXR and CheXpert Plus.
-
Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation
Empirical study finds overconfidence persists in medical VLMs despite scaling and prompting; post-hoc calibration reduces error while hallucination-aware calibration improves both calibration and AUROC.
-
Clinically Aware Synthetic Image Generation for Concept Coverage in Chest X-ray Models
CARPA generates anatomically faithful synthetic chest X-rays with controlled clinical concept insertions and deletions to expand training coverage and improve model precision, calibration, and reliability on real benchmarks.
-
Adapting Foundation Vision-Language Models to Medical Diagnosis via Query-Driven Expert Bridging
MedBridge adapts pretrained VLMs to multi-label medical diagnosis via query tokens for non-destructive alignment and expert routing, reporting 6-15% AUC gains on chest radiograph benchmarks across eight models.
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RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extraction
RA-RRG extracts key phrases with LLMs, retrieves them via multimodal similarity, and conditions report generation on them to achieve SOTA CheXbert scores and competitive RadGraph F1 on MIMIC-CXR and IU X-ray while supporting multi-view inputs.
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Beyond Scalar Scores: Exploring LLM-based Metrics for Clinical Significance Evaluation in Radiology Reports
Lightweight metrics trained on Qwen3-8B and MedGemma-4B using synthetic pairs outperform larger medical LLMs at distinguishing clinical significance in radiology reports while balancing discrimination and robustness.
-
Case-Grounded Evidence Verification: A Framework for Constructing Evidence-Sensitive Supervision
A supervision construction procedure generates explicit support and controlled non-support examples (counterfactual and topic-related negatives) without manual annotation, producing verifiers that demonstrate genuine evidence dependence in radiology tasks.
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Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types
Deep vision models predict health insurance type from normal chest X-rays at AUC ~0.70, indicating capture of socioeconomic signals beyond demographics.
-
RadAgents: Multimodal Agentic Reasoning for Chest X-ray Interpretation with Radiologist-like Workflows
RadAgents is a multi-agent framework coupling clinical priors with task-aware multimodal reasoning and radiologist-like workflows, plus grounding and retrieval-augmentation for conflict resolution in chest X-ray interpretation.
-
MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs
MedXIAOHE is a medical MLLM that claims state-of-the-art benchmark performance through specialized pretraining to cover long-tail diseases and RL-based reasoning training.
-
Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Care
The paper describes Baichuan-M4, a coordinated medical agent system that reports leading scores across static knowledge, dynamic consultation, long-context memory, retrieval, OCR, and multimodal tasks with a 3.3% hallucination rate.