RadVLM Instruction Dataset

We release the RadVLM instruction dataset, a large-scale resource used to
train the RadVLM model on diverse radiology tasks. The dataset contains
1,115,021 image-instruction pairs spanning five task families: (i) report
generation from frontal CXRs using filtered Findings/Impression text; (ii)
abnormality classification for the standard 14 CheXpert labels; (iii) anatomy
grounding; (iv) abnormality detection and grounding; and (v) phrase grounding
from report sentences. To support interactive use, we include ~89k LLM-
generated multi-turn, multi-task conversations (~3k with spatial grounding)
derived from image-linked attributes (reports, labels, boxes). Creation
involved curating datasets from public sources, excluding lateral views,
removing prior-study references and other non-image context from reports,
fusing multi-reader annotations, and harmonizing label and coordinate formats.
The resource is intended for training CXR assistants across diverse radiology
tasks and within a conversational format.