Enhancing 3D Medical Image Understanding with Pretraining Aided by 2D Multimodal Large Language Models
This addresses the need for scalable, annotation-free 3D medical image analysis, potentially enhancing existing networks, though it appears incremental as it builds on existing MLLM and SSL methods.
The paper tackles the problem of limited semantic comprehension in 3D medical image understanding by proposing Med3DInsight, a pretraining framework that integrates 3D image encoders with 2D multimodal large language models, achieving state-of-the-art performance on segmentation and classification tasks across CT and MRI datasets.
Understanding 3D medical image volumes is critical in the medical field, yet existing 3D medical convolution and transformer-based self-supervised learning (SSL) methods often lack deep semantic comprehension. Recent advancements in multimodal large language models (MLLMs) provide a promising approach to enhance image understanding through text descriptions. To leverage these 2D MLLMs for improved 3D medical image understanding, we propose Med3DInsight, a novel pretraining framework that integrates 3D image encoders with 2D MLLMs via a specially designed plane-slice-aware transformer module. Additionally, our model employs a partial optimal transport based alignment, demonstrating greater tolerance to noise introduced by potential noises in LLM-generated content. Med3DInsight introduces a new paradigm for scalable multimodal 3D medical representation learning without requiring human annotations. Extensive experiments demonstrate our state-of-the-art performance on two downstream tasks, i.e., segmentation and classification, across various public datasets with CT and MRI modalities, outperforming current SSL methods. Med3DInsight can be seamlessly integrated into existing 3D medical image understanding networks, potentially enhancing their performance. Our source code, generated datasets, and pre-trained models will be available at https://github.com/Qybc/Med3DInsight.