PadChest-GR: A Bilingual Chest X-ray Dataset for Grounded Radiology Report Generation
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Castro, Daniel C. Bustos, Aurelia Bannur, Shruthi Hyland, Stephanie L. Bouzid, Kenza Wetscherek, Maria Teodora Sánchez-Valverde, Maria Dolores Jaques-Pérez, Lara Pérez-Rodríguez, Lourdes Takeda, Kenji Salinas, José María Alvarez-Valle, Javier Herrero, Joaquín Galant Pertusa, Antonio |
| author_facet | Castro, Daniel C. Bustos, Aurelia Bannur, Shruthi Hyland, Stephanie L. Bouzid, Kenza Wetscherek, Maria Teodora Sánchez-Valverde, Maria Dolores Jaques-Pérez, Lara Pérez-Rodríguez, Lourdes Takeda, Kenji Salinas, José María Alvarez-Valle, Javier Herrero, Joaquín Galant Pertusa, Antonio |
| contents | Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Grounded radiology report generation (GRRG) extends RRG by including the localisation of individual findings on the image. Currently, there are no manually annotated chest X-ray (CXR) datasets to train GRRG models. In this work, we present a dataset called PadChest-GR (Grounded-Reporting) derived from PadChest aimed at training GRRG models for CXR images. We curate a public bi-lingual dataset of 4,555 CXR studies with grounded reports (3,099 abnormal and 1,456 normal), each containing complete lists of sentences describing individual present (positive) and absent (negative) findings in English and Spanish. In total, PadChest-GR contains 7,037 positive and 3,422 negative finding sentences. Every positive finding sentence is associated with up to two independent sets of bounding boxes labelled by different readers and has categorical labels for finding type, locations, and progression. To the best of our knowledge, PadChest-GR is the first manually curated dataset designed to train GRRG models for understanding and interpreting radiological images and generated text. By including detailed localization and comprehensive annotations of all clinically relevant findings, it provides a valuable resource for developing and evaluating GRRG models from CXR images. PadChest-GR can be downloaded under request from https://bimcv.cipf.es/bimcv-projects/padchest-gr/ |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_05085 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PadChest-GR: A Bilingual Chest X-ray Dataset for Grounded Radiology Report Generation Castro, Daniel C. Bustos, Aurelia Bannur, Shruthi Hyland, Stephanie L. Bouzid, Kenza Wetscherek, Maria Teodora Sánchez-Valverde, Maria Dolores Jaques-Pérez, Lara Pérez-Rodríguez, Lourdes Takeda, Kenji Salinas, José María Alvarez-Valle, Javier Herrero, Joaquín Galant Pertusa, Antonio Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Grounded radiology report generation (GRRG) extends RRG by including the localisation of individual findings on the image. Currently, there are no manually annotated chest X-ray (CXR) datasets to train GRRG models. In this work, we present a dataset called PadChest-GR (Grounded-Reporting) derived from PadChest aimed at training GRRG models for CXR images. We curate a public bi-lingual dataset of 4,555 CXR studies with grounded reports (3,099 abnormal and 1,456 normal), each containing complete lists of sentences describing individual present (positive) and absent (negative) findings in English and Spanish. In total, PadChest-GR contains 7,037 positive and 3,422 negative finding sentences. Every positive finding sentence is associated with up to two independent sets of bounding boxes labelled by different readers and has categorical labels for finding type, locations, and progression. To the best of our knowledge, PadChest-GR is the first manually curated dataset designed to train GRRG models for understanding and interpreting radiological images and generated text. By including detailed localization and comprehensive annotations of all clinically relevant findings, it provides a valuable resource for developing and evaluating GRRG models from CXR images. PadChest-GR can be downloaded under request from https://bimcv.cipf.es/bimcv-projects/padchest-gr/ |
| title | PadChest-GR: A Bilingual Chest X-ray Dataset for Grounded Radiology Report Generation |
| topic | Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.05085 |