Anatomical grounding pre-training for medical phrase grounding
Journal:
arXiv
Published Date:
Feb 23, 2025
Abstract
Medical Phrase Grounding (MPG) maps radiological findings described in
medical reports to specific regions in medical images. The primary obstacle
hindering progress in MPG is the scarcity of annotated data available for
training and validation. We propose anatomical grounding as an in-domain
pre-training task that aligns anatomical terms with corresponding regions in
medical images, leveraging large-scale datasets such as Chest ImaGenome. Our
empirical evaluation on MS-CXR demonstrates that anatomical grounding
pre-training significantly improves performance in both a zero-shot learning
and fine-tuning setting, outperforming state-of-the-art MPG models. Our
fine-tuned model achieved state-of-the-art performance on MS-CXR with an mIoU
of 61.2, demonstrating the effectiveness of anatomical grounding pre-training
for MPG.
