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LATTE-CXR: Locally Aligned TexT and imagE, Explainable dataset for Chest X-Rays

Elham Ghelichkhan, Tolga Tasdizen

This dataset includes bounding box-statement pairs for chest X-ray images, derived from radiologists’ eye-tracking data (for explainability) and annotations, for local visual-language models.

eye-tracking chest x-ray dataset automatically generated dataset caption-guided object detection image captioning with region-level description grounded radiology report generation phrase grounding xai multi-modal learning local visual-language models localization

Published: Feb. 4, 2025. Version: 1.0.0

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Lunguage: A Benchmark for Structured and Sequential Chest X-ray Interpretation

Jong Hak Moon, Geon Choi, Paloma Rabaey, et al.

A radiologist-annotated benchmark of structured chest X-ray reports at single and sequential levels, comprising 1,473 reports across 18 relation types and 80 longitudinal cases.

fine-grained structured reports attribute-level clinical reasoning medical text structuring longitudinal clinical reasoning chest x-ray report parsing medical information structuring benchmark dataset for radiology report medical information extraction structured radiology reports temporal relation extraction radiology report benchmarking longitudinal clinical understanding

Published: Jan. 11, 2026. Version: 1.0.0

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CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-rays

Hyungyung Lee, Geon Choi, Jung Oh Lee, et al.

CheXStruct is an automated pipeline that derives structured diagnostic reasoning steps from chest X-rays. CXReasonBench builds on this to evaluate whether models perform clinically grounded, multi-step reasoning beyond final diagnoses.

evaluation chest x-ray benchmark structured chest x-ray qa intermediate reasoning steps structured reasoning grounded reasoning diagnostic reasoning structured diagnostic pipeline

Published: Oct. 23, 2025. Version: 1.0.1

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Medical-Diff-VQA: A Large-Scale Medical Dataset for Difference Visual Question Answering on Chest X-Ray Images

Xinyue Hu, Lin Gu, Qiyuan An, et al.

MIMIC-Diff-VQA provides a large-scale dataset for Difference visual question answering in medical chest x-ray images.

difference visual question answering difference vqa vqa chest x-ray visual question answering

Published: Feb. 3, 2025. Version: 1.0.1

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RadGraph2: Tracking Findings Over Time in Radiology Reports

Adam Dejl, Sameer Khanna, Patricia Therese Pile, et al.

RadGraph2 is a dataset of 800 chest radiology reports annotated using a fine-grained entity-relationship schema, which captures key findings as well as mentions of changes that occurred in comparison with the previous radiology studies.

relation extraction chest x-rays disease progression information extraction radiology reports named entity recognition

Published: Aug. 8, 2024. Version: 1.0.0

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Radiology Report Expert Evaluation (ReXVal) Dataset

Feiyang Yu, Mark Endo, Rayan Krishnan, et al.

The Radiology Report Expert Evaluation (ReXVal) Dataset is a publicly available dataset of radiologist evaluations of errors in automatically generated radiology reports.

Published: June 20, 2023. Version: 1.0.0

Database Restricted Access

REFLACX: Reports and eye-tracking data for localization of abnormalities in chest x-rays

Ricardo Bigolin Lanfredi, Mingyuan Zhang, William Auffermann, et al.

This dataset contains 3032 cases of eye-tracking data collected while five radiologists dictated reports for frontal chest x-rays, synchronized timestamped dictation transcription, and manual labels for validation of localization of abnormalities.

eye tracking radiology report reflacx fixations computer vision gaze chest x-rays radiology machine learning deep learning

Published: Sept. 27, 2021. Version: 1.0.0

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RadNLI: A natural language inference dataset for the radiology domain

Yasuhide Miura, Yuhao Zhang, Emily Tsai, et al.

A radiology NLI dataset introduced in the paper: Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation

Published: June 29, 2021. Version: 1.0.0

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Eye Gaze Data for Chest X-rays

Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou, et al.

This dataset was a collected using an eye tracking system while a radiologist interpreted and read 1,083 public CXR images. The dataset contains the following aligned modalities: image, transcribed report text, dictation audio and eye gaze data.

convolutional network heatmap eye tracking explainability audio chest cxr chest x-ray radiology multimodal machine learning deep learning

Published: Sept. 12, 2020. Version: 1.0.0

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CXR-Align: A Benchmark for CXR-Report Alignment with Negations

Hanbin Ko

CXR-Align is a benchmark dataset created to evaluate vision-language models' capability to interpret negations in chest X-ray (CXR) reports, featuring systematically modified reports from MIMIC-CXR.

Published: Aug. 21, 2025. Version: 1.0.0