VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine

TL;DR

VELVET-Med introduces a novel vision-language pre-training framework tailored for volumetric medical imaging, leveraging self-supervised learning and hierarchical contrastive objectives to improve performance on diverse downstream tasks with limited data.

Contribution

It presents a new VLP framework with a specialized language encoder and hierarchical contrastive learning, addressing data scarcity in volumetric medical imaging.

Findings

Achieves state-of-the-art results on multiple downstream tasks.

Effectively learns from only 38,875 scan-report pairs.

Enhances generalization and transferability of medical image encoders.

Abstract

Vision-and-language models (VLMs) have been increasingly explored in the medical domain, particularly following the success of CLIP in general domain. However, unlike the relatively straightforward pairing of 2D images and text, curating large-scale paired data in the medical field for volumetric modalities such as CT scans remains a challenging and time-intensive process. This difficulty often limits the performance on downstream tasks. To address these challenges, we propose a novel vision-language pre-training (VLP) framework, termed as \textbf{VELVET-Med}, specifically designed for limited volumetric data such as 3D CT and associated radiology reports. Instead of relying on large-scale data collection, our method focuses on the development of effective pre-training objectives and model architectures. The key contributions are: 1) We incorporate uni-modal self-supervised learning into VLP framework, which are often underexplored in the existing literature. 2) We propose a novel language encoder, termed as \textbf{TriBERT}, for learning multi-level textual semantics. 3) We devise the hierarchical contrastive learning to capture multi-level vision-language correspondence. Using only 38,875 scan-report pairs, our approach seeks to uncover rich spatial and semantic relationships embedded in volumetric medical images and corresponding clinical narratives, thereby enhancing the generalization ability of the learned encoders. The resulting encoders exhibit strong transferability, achieving state-of-the-art performance across a wide range of downstream tasks, including 3D segmentation, cross-modal retrieval, visual question answering, and report generation.