Towards Scalable Language-Image Pre-training for 3D Medical Imaging
Authors: name: Chenhui Zhao, name: Yiwei Lyu, name: Asadur Chowdury, name: Edward Harake, name: Akhil Kondepudi, name: Akshay Rao, name: Xinhai Hou, name: Honglak Lee, name: Todd Hollon

Language-image pre-training has demonstrated strong performance in 2D medical
imaging, but its success in 3D modalities such as CT and MRI remains limited
due to the high computational demands of volumetric data, which pose a
significant barrier to training on large-scale, uncurated clinical studies. In
this study, we introduce Hierarchical attention for Language-Image Pre-training
(HLIP), a scalable pre-training framework for 3D medical imaging. HLIP adopts a
lightweight hierarchical attention mechanism inspired by the natural hierarchy
of radiology data: slice, scan, and study. This mechanism exhibits strong
generalizability, e.g., +4.3% macro AUC on the Rad-ChestCT benchmark when
pre-trained on CT-RATE. Moreover, the computational efficiency of HLIP enables
direct training on uncurated datasets. Trained on 220K patients with 3.13
million scans for brain MRI and 240K patients with 1.44 million scans for head
CT, HLIP achieves state-of-the-art performance, e.g., +32.4% balanced ACC on
the proposed publicly available brain MRI benchmark Pub-Brain-5; +1.4% and
+6.9% macro AUC on head CT benchmarks RSNA and CQ500, respectively. These
results demonstrate that, with HLIP, directly pre-training on uncurated
clinical datasets is a scalable and effective direction for language-image
pre-training in 3D medical imaging. The code is available at
https://github.com/Zch0414/hlip