  With the rapid development of multimodal large language models (MLLMs),
especially their capabilities in visual chat through refer and ground
functionalities, their significance is increasingly recognized. However, the
biomedical field currently exhibits a substantial gap in this area, primarily
due to the absence of a dedicated refer and ground dataset for biomedical
images. To address this challenge, we devised the Med-GRIT-270k dataset. It
comprises 270k question-and-answer pairs and spans eight distinct medical
imaging modalities. Most importantly, it is the first dedicated to the
biomedical domain and integrating refer and ground conversations. The key idea
is to sample large-scale biomedical image-mask pairs from medical segmentation
datasets and generate instruction datasets from text using chatGPT.
Additionally, we introduce a Refer-and-Ground Multimodal Large Language Model
for Biomedicine (BiRD) by using this dataset and multi-task instruction
learning. Extensive experiments have corroborated the efficacy of the
Med-GRIT-270k dataset and the multi-modal, fine-grained interactive
capabilities of the BiRD model. This holds significant reference value for the
exploration and development of intelligent biomedical assistants.
