NEATX: Non-Expert Annotations of Tubes in X-rays

The Non-Expert Annotations of Tubes in X-rays (NEATX) dataset was created at PURRlab as part of a MSc thesis of Trine Naja Eriksen and Cathrine Damgaard. This dataset contains 3.5k chest drain annotations for the NIH-CXR14 dataset, and 1k annotations for four different tube types (chest drain, tracheostomy, nasogastric, and endotracheal) in the PadChest dataset by two data science students. Please read more about how the dataset can be used in https://arxiv.org/abs/2309.02244. Bibtex: @article{damgaard2023augmenting, title={Augmenting chest x-ray datasets with non-expert annotations}, author={Damgaard, Cathrine and Eriksen, Trine Naja and Juodelyte, Dovile and Cheplygina, Veronika and Jim{\'e}nez-S{\'a}nchez, Amelia}, journal={arXiv preprint arXiv:2309.02244}, year={2023} } Data description: This dataset contains the annotations provided by two data science students (not medical experts) for: A csv file with 3.5k chest drain annotations for NIH-CXR14 dataset A csv file with 1k annotations for four different tube types (chest drain, tracheostomy, nasogastric, and endotracheal) for PadChest dataset We provide the raw individual annotations as well as the aggregated annotations. The annotation protocol is described in the Healthsheet

NeRF-To-Real Tester: Neural Radiance Fields as Test Image Generators for Vision of Autonomous Systems

NeRF-To-Real Tester is a testing software package designed to generate test images generated from Neural Radiance Fields (NeRF), created in nerfstudio, and use NeRF-generated images to evaluate image processing Systems Under Test (SUTs). SUTs include various feature extractors like interest point detectors as well as image classifiers. This system assesses SUTs based on several metrics such as repeatability, interest point spread, L2 norm, and cosine similarity