Multi-Granularity Graph-Mamba Multi-Instance Learning for Unlabeled Autofluorescence Whole-Slide Image Classification

Multi-instance learning (MIL) has significantly advanced AI-assisted cancer diagnosis using histochemically stained whole-slide images (WSIs). However, acquiring such WSIs is time-consuming, labor-intensive, and environmentally unfriendly. To address this, we propose using unlabeled autofluorescence (UAF) WSIs as a cost-effective alternative for MIL-based diagnosis. We introduce a dedicated UAF WSI dataset, LCUHI-UAF, along with a matched H&E-stained WSI dataset, LCUHI-H&E, derived from the same tissue sections for direct comparison. To tackle the low signal-to-noise ratio and blurred morphological details in UAF images for classification tasks, we propose a novel Multi-Granularity Graph-Mamba (MGGM) MIL framework. In this framework, each WSI is represented as a graph constructed from the spatial coordinates of tissue instances, allowing graph convolution to capture local spatial dependencies while the Mamba architecture models long-range relationships. A multi-granularity mechanism is further proposed to enable comprehensive representation of hierarchical relationships among cell clusters and microenvironments within the tissue. Experiments on the paired lung cancer datasets LCUHI-H& E and LCUHI-UAF show that MGGM-MIL achieves top-performing results across two pre-trained feature settings. These results simultaneously confirm the viability of UAF-stained WSIs as a cost-effective substitute for H&E in cancer diagnosis. Additional evaluation on the Camelyon16 breast cancer dataset further validates the generalizability of MGGM-MIL across tissue types. Code is available at https://github.com/JiuyangDong/MGGMMIL.