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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.10004 |
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| _version_ | 1866914838790275072 |
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| author | Rückert, Johannes Bloch, Louise Brüngel, Raphael Idrissi-Yaghir, Ahmad Schäfer, Henning Schmidt, Cynthia S. Koitka, Sven Pelka, Obioma Abacha, Asma Ben de Herrera, Alba G. Seco Müller, Henning Horn, Peter A. Nensa, Felix Friedrich, Christoph M. |
| author_facet | Rückert, Johannes Bloch, Louise Brüngel, Raphael Idrissi-Yaghir, Ahmad Schäfer, Henning Schmidt, Cynthia S. Koitka, Sven Pelka, Obioma Abacha, Asma Ben de Herrera, Alba G. Seco Müller, Henning Horn, Peter A. Nensa, Felix Friedrich, Christoph M. |
| contents | Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Access subset. It is an updated version of the ROCO dataset published in 2018, and adds 35,705 new images added to PMC since 2018. It further provides manually curated concepts for imaging modalities with additional anatomical and directional concepts for X-rays. The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical Caption 2023. The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using Unified Medical Language System (UMLS) concepts provided with each image. In addition, it can serve for pre-training of medical domain models, and evaluation of deep learning models for multi-task learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10004 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset Rückert, Johannes Bloch, Louise Brüngel, Raphael Idrissi-Yaghir, Ahmad Schäfer, Henning Schmidt, Cynthia S. Koitka, Sven Pelka, Obioma Abacha, Asma Ben de Herrera, Alba G. Seco Müller, Henning Horn, Peter A. Nensa, Felix Friedrich, Christoph M. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Access subset. It is an updated version of the ROCO dataset published in 2018, and adds 35,705 new images added to PMC since 2018. It further provides manually curated concepts for imaging modalities with additional anatomical and directional concepts for X-rays. The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical Caption 2023. The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using Unified Medical Language System (UMLS) concepts provided with each image. In addition, it can serve for pre-training of medical domain models, and evaluation of deep learning models for multi-task learning. |
| title | ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2405.10004 |