  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.
