Large Model Driven Multi-Granularity Medical Image Analysis: A Fuzzy Logic-Guided Framework

The analysis of medical images requires sophisticated computational approaches that can handle the inherent complexity and uncertainty present in pathological structures. This paper presents a large model driven framework that integrates fuzzy logic principles with transformer-based architectures to enable multi-granularity medical image analysis. The proposed approach, termed ULVM-MG, employs a sophisticated feature extraction strategy that simultaneously processes pathological images at coarse, medium, and fine granularity levels, mirroring the systematic examination methodology employed by experienced pathologists. In particular, a fuzzy-guided cross-attention mechanism directs the transformer's attention toward diagnostically significant regions while preserving essential contextual information. regions while preserving essential contextual information. Comprehensive evaluation on histopathological datasets demonstrates superior performance compared to state-of-the-art transformer-based approaches. ULVM-MG achieves 98.76% and 97.34% accuracy on LC25000 and NCT datasets, respectively, outperforming the best baseline by 1.61% and 2.17%. The framework excels particularly in distinguishing morphologically similar tissue types and benign versus malignant classification tasks. Ablation studies confirm the critical contributions of multi-granularity processing and fuzzy uncertainty modeling, with statistical analysis revealing significant performance improvements across all evaluation metrics.