HSENet: Hybrid Spatial Encoding Network for 3D Medical Vision-Language Understanding (2506.09634v1)

Abstract: Automated 3D CT diagnosis empowers clinicians to make timely, evidence-based decisions by enhancing diagnostic accuracy and workflow efficiency. While multimodal LLMs (MLLMs) exhibit promising performance in visual-language understanding, existing methods mainly focus on 2D medical images, which fundamentally limits their ability to capture complex 3D anatomical structures. This limitation often leads to misinterpretation of subtle pathologies and causes diagnostic hallucinations. In this paper, we present Hybrid Spatial Encoding Network (HSENet), a framework that exploits enriched 3D medical visual cues by effective visual perception and projection for accurate and robust vision-language understanding. Specifically, HSENet employs dual-3D vision encoders to perceive both global volumetric contexts and fine-grained anatomical details, which are pre-trained by dual-stage alignment with diagnostic reports. Furthermore, we propose Spatial Packer, an efficient multimodal projector that condenses high-resolution 3D spatial regions into a compact set of informative visual tokens via centroid-based compression. By assigning spatial packers with dual-3D vision encoders, HSENet can seamlessly perceive and transfer hybrid visual representations to LLM's semantic space, facilitating accurate diagnostic text generation. Experimental results demonstrate that our method achieves state-of-the-art performance in 3D language-visual retrieval (39.85% of R@100, +5.96% gain), 3D medical report generation (24.01% of BLEU-4, +8.01% gain), and 3D visual question answering (73.60% of Major Class Accuracy, +1.99% gain), confirming its effectiveness. Our code is available at https://github.com/YanzhaoShi/HSENet.

An Expert Overview of HSENet: Hybrid Spatial Encoding Network for 3D Medical Vision-Language Understanding

The paper entitled "HSENet: Hybrid Spatial Encoding Network for 3D Medical Vision-Language Understanding" introduces a novel framework to address the challenges inherent in 3D medical image analysis and report generation using Vision-LLMs. It points out the limitations of existing multimodal LLMs primarily trained on 2D images, which struggle to capture the complex spatial and anatomical structures present in 3D medical imaging contexts such as CT scans. These limitations potentially lead to diagnostic inaccuracies and hallucinations—an area where HSENet seeks to make substantial contributions.

Core Contributions and Methodology

HSENet leverages enriched 3D medical visual cues through dual-stage alignment in a dual-3D vision encoder setup. The framework splits visual perception into two core tasks: capturing global volumetric contexts and refining fine-grained anatomical details with two separate 3D vision encoders. The use of dual-stage alignment ensures that these encoders are well adapted to the multi-scale and intricate nature of medical imaging.

Additionally, the paper introduces the concept of the Spatial Packer—a specialized multimodal projector aimed at efficiently compressing high-resolution 3D spatial regions into informative visual tokens. This process involves a centroid-based compression mechanism that captures the essence of spatial information, ensuring that the LLM receives a meaningful and rich representation that can facilitate accurate diagnostic report generation.

Significant Numerical Outcomes

Several key numerical results underscore the efficacy of HSENet across various medical imaging and language tasks:

1. 3D Language-Visual Retrieval: HSENet achieves a Recall@100 of 39.85%, marking a 5.96% improvement over prior art. This statistic underscores its effectiveness in accurately pairing medical images with corresponding language reports.
2. 3D Medical Report Generation: With a BLEU-4 score of 24.01%, HSENet leads the domain with an 8.01% gain compared to existing solutions. This demonstrates the framework's robustness in generating coherent and clinically relevant medical narratives from 3D volumes.
3. Visual Question Answering (VQA): The model outperforms peers with a Major Class Accuracy of 73.60%, refining accuracy in answering complex visual queries related to spatial reasonings over 3D anatomical structures by 1.99%.

These results illustrate robust advancements in 3D spatial understanding and reinforce HSENet's ability to reduce diagnostic hallucinations by maintaining clarity in diagnostic interpretations.

Implications and Future Research Directions

The implications of this research are profound, both practically and theoretically. Enhancing 3D medical imaging interpretation could lead to more accurate diagnostics and treatment planning, significantly reducing the cognitive load on radiologists and improving clinical outcomes. Moreover, a theoretical foundation in hybrid spatial encoding could pave the way for future developments in 3D image analyses across various domains, not limited to just healthcare.

Future research might explore optimizing the encoder strategies to further reduce data overhead while maintaining efficacy. It could also explore integration with electronic health records (EHR) for incorporating contextual patient data, thus enhancing model interpretability and diagnostic precision.

Overall, while HSENet represents a substantial advancement in 3D medical imaging analysis, continuous exploration into more dynamic and adaptable frameworks—potentially incorporating real-time learning and feedback mechanisms—can further elevate the practical utility of AI in medical contexts.