CoMIL: A Contrastive CNN-Transformer Framework with Multi-Instance Learning for Whole-Slide Pathology Image Classification

Whole slide image (WSI) classification faces challenges due to gigapixel scale and weak supervision, often struggling to balance global context with local details. We propose CoMIL, a dual-branch framework based on symmetric mutual learning. Firstly, to resolve the dilemma where single-stream networks struggle to simultaneously capture global context and fine-grained details, we employ dual parallel pathways: a Transformer branch models long-range instance dependencies, while a CNN branch captures localized tissue morphology. Secondly, to address spatial information loss, we design a Hyper Positional Generator (HyperPG). This module integrates multi-scale adaptive mechanisms with deformable convolutions, enhancing spatial awareness with linear complexity. Finally, to improve model robustness against weak label noise, bidirectional learning between branches is achieved through KL divergence minimization. Extensive experiments show that our proposed method achieves an area under the curve of 98.6% and an accuracy of 95.3% on the Camelyon16 dataset, and an area under the curve of 98.8% and an accuracy of 93.3% on the TCGA_Kidney dataset, surpassing the performance of known advanced WSI classification methods.