BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval
Yinda Chen Che Liu Xiaoyu Liu Rossella Arcucci Zhiwei Xiong
The burgeoning integration of 3D medical imaging into healthcare has led to a
substantial increase in the workload of medical professionals. To assist
clinicians in their diagnostic processes and alleviate their workload, the
development of a robust system for retrieving similar case studies presents a
viable solution. While the concept holds great promise, the field of 3D medical
text-image retrieval is currently limited by the absence of robust evaluation
benchmarks and curated datasets. To remedy this, our study presents a
groundbreaking dataset, BIMCV-R (This dataset will be released upon
acceptance.), which includes an extensive collection of 8,069 3D CT volumes,
encompassing over 2 million slices, paired with their respective radiological
reports. Expanding upon the foundational work of our dataset, we craft a
retrieval strategy, MedFinder. This approach employs a dual-stream network
architecture, harnessing the potential of large language models to advance the
field of medical image retrieval beyond existing text-image retrieval
solutions. It marks our preliminary step towards developing a system capable of
facilitating text-to-image, image-to-text, and keyword-based retrieval tasks.