Semantic-Topological Graph Reasoning for Language-Guided Pulmonary Screening
Summary: arXiv:2604.05620v1 Announce Type: cross
Introduction
Medical image segmentation driven by free-text clinical instructions has emerged as a critical frontier in computer-aided diagnosis. The intersection of natural language processing and medical imaging offers significant potential for enhancing diagnostic accuracy. However, existing multimodal and foundation models face challenges in interpreting the semantic ambiguity present in clinical reports. Furthermore, these models often struggle with complex anatomical overlaps in low-contrast scans, limiting their practical applications in real-world scenarios.
Challenges in Current Models
Current models are typically designed to operate under ideal conditions, yet they often falter when faced with the intricacies of medical data. Key challenges include:
- Semantic Ambiguity: Clinical reports frequently contain ambiguous terms that can lead to misinterpretation.
- Anatomical Overlaps: Low-contrast scans can complicate the identification of distinct anatomical structures.
- Overfitting: Fully fine-tuning massive architectures on limited medical datasets often results in severe overfitting, reducing their generalizability.
The Proposed Framework: Semantic-Topological Graph Reasoning (STGR)
To address these pressing challenges, we introduce the Semantic-Topological Graph Reasoning (STGR) framework for language-guided pulmonary screening. This novel approach synergizes the reasoning capabilities of large language models (LLaMA-3-V) with the zero-shot delineation of vision foundation models (MedSAM).
Key Components of STGR
The STGR framework comprises several innovative components designed to enhance diagnostic accuracy:
- Text-to-Vision Intent Distillation (TVID): This module extracts precise diagnostic guidance from clinical instructions, facilitating improved decision-making in segmentation tasks.
- Dynamic Graph Reasoning: To resolve anatomical ambiguity, mask selection is formulated as a dynamic graph reasoning problem. Candidate lesions are modeled as nodes while edges represent spatial and semantic affinities.
- Selective Asymmetric Fine-Tuning (SAFT): This strategy updates less than 1% of the parameters, ensuring deployment feasibility and reducing the risk of overfitting.
Results and Performance
Our rigorous evaluation using 5-fold cross-validation on the LIDC-IDRI and LNDb datasets demonstrates that the STGR framework establishes a new state-of-the-art in language-guided pulmonary screening. Notably, our framework achieves an impressive 81.5% Dice Similarity Coefficient (DSC) on the LIDC-IDRI dataset, outperforming leading LLM-based tools such as LISA by over 5%.
Moreover, the SAFT strategy serves as a powerful regularizer, resulting in exceptional cross-fold stability with only 0.6% DSC variance. This stability is crucial for enabling robust and context-aware clinical deployment.
Conclusion
The Semantic-Topological Graph Reasoning (STGR) framework represents a significant advancement in the field of medical image segmentation. By addressing the challenges of semantic ambiguity and anatomical overlap, STGR paves the way for more accurate and reliable computer-aided diagnostic systems, ultimately contributing to better patient outcomes in pulmonary screening.