CellMixer: Pathological image classification using dual-branch VMamba with randomly mixing gradient features data augmentation

• Fuses gradient-pixel features to amplify subtle pathological distinctions. • Jointly captures tissue-context and cellular details via dual-branch modeling. • Integrates multi-scale features for class uniformity-variability representation. • SOTA over datasets against costy foundational models Pathological diagnosis is crucial for patient care, and Region of Interest (ROI) analysis serves as a key pathological method for extracting local cellular details to guide precise clinical decision-making. While most of the current foundation models have shown promise in ROI pathological image classification, existing approaches often fall short in addressing the unique characteristics of pathology ROI data from three aspects simultaneously: (1) inter-class similarity, (2) complex global patterns, and (3) multi-scale granularity. To address them, we propose CellMixer, a novel framework designed to extract and integrate local-global ROI pathological image representations. The key innovation lies in the synergistic integration of three corresponding domain-aware components: (1) To amplify subtle morphological distinctions, we designed a data augmentation (GradMix), which selectively fuses gradient maps and pixel-level features to enhances low-level feature sensitivity, directly improving discrimination of visually similar classes; (2) To capture both localized patterns and global tissue structures across ROI regions, we proposed Dual-branch VMamba Block (DVB), which enhances long-range dependency modeling and simultaneously extracts cell-level fine-grained features; (3) To fuse local and global features to concurrently represent intra-class homogeneity and inter-class heterogeneity across scales, a novel feature fusion strategy (Insert-Merge (InM)). Extensive experiments on 8 public pathology ROI datasets demonstrate that CellMixer consistently outperforms existing methods, proving task-specific model, even with limited data, yields superior visual representations to generic foundation models.