SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification

• URL: http://arxiv.org/abs/2604.15711v1
• Date: Fri, 17 Apr 2026 05:32:28 GMT
• Title: SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification
• Abstract summary: We propose SSMamba, a hybrid SSL framework that enables effective fine-grained feature learning without relying on large external datasets.<n>SSMamba outperforms 11 state-of-the-art pathological FMs on 10 public ROI datasets and surpasses 8 SOTA methods on 6 public WSI datasets.
• Score: 21.197869098405416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pathological diagnosis is highly reliant on image analysis, where Regions of Interest (ROIs) serve as the primary basis for diagnostic evidence, while whole-slide image (WSI)-level tasks primarily capture aggregated patterns. To extract these critical morphological features, ROI-level Foundation Models (FMs) based on Vision Transformers (ViTs) and large-scale self-supervised learning (SSL) have been widely adopted. However, three core limitations remain in their application to ROI analysis: (1) cross-magnification domain shift, as fixed-scale pretraining hinders adaptation to diverse clinical settings; (2) inadequate local-global relationship modeling, wherein the ViT backbone of FMs suffers from high computational overhead and imprecise local characterization; (3) insufficient fine-grained sensitivity, as traditional self-attention mechanisms tend to overlook subtle diagnostic cues. To address these challenges, we propose SSMamba, a hybrid SSL framework that enables effective fine-grained feature learning without relying on large external datasets. This framework incorporates three domain-adaptive components: Mamba Masked Image Modeling (MAMIM) for mitigating domain shift, a Directional Multi-scale (DMS) module for balanced local-global modeling, and a Local Perception Residual (LPR) module for enhanced fine-grained sensitivity. Employing a two-stage pipeline, SSL pretraining on target ROI datasets followed by supervised fine-tuning (SFT), SSMamba outperforms 11 state-of-the-art (SOTA) pathological FMs on 10 public ROI datasets and surpasses 8 SOTA methods on 6 public WSI datasets. These results validate the superiority of task-specific architectural designs for pathological image analysis.

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