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.