Resources

17 results for "automatically generated dataset"

Database Open Access

Radiology Report Generation Models Evaluation Dataset For Chest X-rays (RadEvalX)

Amos Rubin Calamida, Farhad Nooralahzadeh, Morteza Rohanian, et al.

The Radiology Report Generation Models Evaluation Dataset For Chest X-rays (RadEvalX) is publicly available and developed similarly to the ReXVal dataset. Just like ReXVal, RadEvalX focuses on radiologist evaluations of errors found in automatically…
Published: June 18, 2024. Version: 1.0.0
Database Restricted Access

CXRGraph: Using Information Extraction to Normalize the Training Data for Automatic Radiology Report Generation

Yuxiang Liao, Hoisang Heung, Hantao Liu, et al.

CXRGraph is a dataset of structured radiology reports dataset following the RadGraph format, which has been tailored for the Automatic Radiology Report Generation (ARRG) task. CXRGraph assorts clinical information from full-text radiology reports in…
Published: Feb. 3, 2025. Version: 1.0.0
Database Credentialed Access

ReFiSco: Report Fix and Score Dataset for Radiology Report Generation

Katherine Tian, Sina J Hartung, Andrew A Li, et al.

Automated generation of clinically accurate radiology reports can improve patient care. In order to improve automatic report generation, it is helpful to understand what types of errors are common in generated reports. Thus, we introduce the Report …
Published: Aug. 23, 2023. Version: 1.0
Database Credentialed Access

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. The dataset contains annotations from 6 board certified radiologists on clinic…
Published: June 20, 2023. Version: 1.0.0
Database Restricted Access

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

Elham Ghelichkhan, Tolga Tasdizen

Local annotation of medical data is both expensive and time-consuming due to the high cost of expert annotators, the precision required for accurate annotation, and the inherent challenges of medical diagnosis. To address these problems, we develop…
Published: Feb. 4, 2025. Version: 1.0.0
Database Credentialed Access

FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark

Mingjie Li, Wenjia Cai, Rui Liu, et al.

Automatic medical report generation (MRG) towards describing life-threatening lesions from given medical images, such as Chest X-ray and Fundus Fluorescein Angiography (FFA), has been a long-standing research topic in machine learning and automatic …
Published: Jan. 21, 2025. Version: 1.1.0
Database Credentialed Access

RaDialog Instruct Dataset

Chantal Pellegrini, Ege Özsoy, Benjamin Busam, et al.

Conversational AI tools that can generate and discuss clinically correct radiology reports for a given medical image have the potential to transform radiology. Such a human-in-the-loop radiology assistant could facilitate a collaborative diagnostic …
Published: July 12, 2024. Version: 1.1.0
Database Open Access

MAMA-MIAAI - Here We Go Again: An Extended Breast MRI Dataset with Bias Field Correction and Automated Breast Tissue Segmentation for Radiomics and AI Research

Florian Schwarzhans, Palak Handa, Laura Atenea Villazan Garcia, et al.

The publicly available MAMA-MIA breast dynamic contrast-enhanced magnetic resonance imaging (MRI) dataset has become an important resource for artificial intelligence (AI) research on breast cancer, particularly for tumor detection and predicting pa…
Published: Oct. 13, 2026. Version: 1.0.0
Database Credentialed Access

Chest ImaGenome Dataset

Joy Wu, Nkechinyere Agu, Ismini Lourentzou, et al.

In recent years, with the release of multiple large datasets, automatic interpretation of chest X-ray (CXR) images with deep learning models have become feasible for specific abnormalities or for generating preliminary reports. However, despite repo…
Published: July 13, 2021. Version: 1.0.0
Database Credentialed Access

Annotated Question-Answer Pairs for Clinical Notes in the MIMIC-III Database

Xiang Yue, Xinliang Frederick Zhang, Huan Sun

Clinical question answering (QA) (or reading comprehension) aims to automatically answer questions from medical professionals based on clinical texts. We release this dataset, which contains 1287 annotated QA pairs on 36 sampled discharge summaries …
Published: Jan. 15, 2021. Version: 1.0.0