Abstract
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.
Louise Bloch
Associated Researcher
My research interests include interpretable machine learning, mutlimodal deep learning, and medical image processing.
Raphael Brüngel
Associated Researcher
My research interests include artificial intelligence, computational linguistics, and information retrieval.
Ahmad Idrissi-Yaghir
Researcher in the first cohort
My research interests include Deep Learning, Natural Language Processing, and Information Retrieval.
Henning Schäfer
Researcher in the first cohort
My research interests include Deep Learning, Computer Vision, Radiomics, and Explainable AI.
Obioma Pelka
Associated Researcher
Peter Horn
Principal Investigator
My research interests include Transfusion Medicine, Immunology, and Bioinformatics.
Felix Nensa
Speaker
My research interests include medical digitalization, computer vision and radiology.
Christoph M. Friedrich
Co-Speaker
My research interests include Deep Learning, Computer Vision, Radiomics, and Explainable AI.