mlsanityData to 14 Oct 2026

Vim4Path: Self-Supervised Vision Mamba for Histopathology Images

arXiv 2404.13222 ↗First posted 20 Apr 2024eess.IV cs.CVcited by 19

On the map: State-space sequence models (S4, Mamba) → Mamba for medical image analysis

Abstract (arXiv)

Representation learning from Gigapixel Whole Slide Images (WSI) poses a significant challenge in computational pathology due to the complicated nature of tissue structures and the scarcity of labeled data. Multi-instance learning methods have addressed this challenge, leveraging image patches to classify slides utilizing pretrained models using Self-Supervised Learning (SSL) approaches. The performance of both SSL and MIL methods relies on the architecture of the feature encoder. This paper proposes leveraging the Vision Mamba (Vim) architecture, inspired by state space models, within the DINO framework for representation learning in computational pathology. We evaluate the performance of Vim against Vision Transformers (ViT) on the Camelyon16 dataset for both patch-level and slide-level classification. Our findings highlight Vim's enhanced performance compared to ViT, particularly at smaller scales, where Vim achieves an 8.21 increase in ROC AUC for models of similar size. An explainability analysis further highlights Vim's capabilities, which reveals that Vim uniquely emulates the pathologist workflow-unlike ViT. This alignment with human expert analysis highlights Vim's potential in practical diagnostic settings and contributes significantly to developing effective representation-learning algorithms in computational pathology. We release the codes and pretrained weights at urlhttps://github.com/AtlasAnalyticsLab/Vim4Path.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 33 of its 48 references that are in the map. First posted on arXiv 20 Apr 2024.

Builds on

33 of 48 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology11 Mar
  2. 2024MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models8 Mar
  3. 2023Structured State Space Models for Multiple Instance Learning in Digital Pathology27 Jun

Other references · 30

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  4. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  5. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  6. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  7. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  8. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  9. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  10. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,287
  11. 2021An Empirical Study of Training Self-Supervised Vision Transformerscited by 1,616
  12. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,380
18 more
  1. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  2. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,056
  3. 2016Pixel Recurrent Neural Networkscited by 816
  4. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  5. 2020HiPPO: Recurrent Memory with Optimal Polynomial Projectionscited by 605
  6. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  7. 2022Hungry Hungry Hippos: Towards Language Modeling with State Space Modelscited by 499
  8. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  9. 2022On the Parameterization and Initialization of Diagonal State Space Modelscited by 424
  10. 2018Rotation Equivariant CNNs for Digital Pathologycited by 422
  11. 2023Hyena Hierarchy: Towards Larger Convolutional Language Modelscited by 393
  12. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 359
  13. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 256
  14. 2022Benchmarking Self-Supervised Learning on Diverse Pathology Datasetscited by 150
  15. 2020Self supervised contrastive learning for digital histopathologycited by 146
  16. 2023Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classificationcited by 60
  17. 2023Computational Pathology: A Survey Review and The Way Forwardcited by 30
  18. 2022S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learningcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 9; 2026: 5.

19 papers.

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  1. 2024Visual Mamba: A Survey and New Outlookscited by 41
  2. 2024DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensingcited by 20
  3. 2024Vision Mamba for Classification of Breast Ultrasound Imagescited by 7
  4. 2024MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentationcited by 7
  5. 2025VSRM: A Robust Mamba-Based Framework for Video Super-Resolutioncited by 5
  6. 2025Discovering Pathology Rationale and Token Allocation for Efficient Multimodal Pathology Reasoningcited by 4
  7. 2024Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision Mambacited by 2
  8. 2025LEAF-Mamba: Local Emphatic and Adaptive Fusion State Space Model for RGB-D Salient Object Detectioncited by 2
  9. 2025MVHybrid: Improving Spatial Transcriptomics Prediction with Hybrid State Space-Vision Transformer Backbone in Pathology Vision Foundation Modelscited by 1
  10. 2025Multiple Instance Learning Framework with Masked Hard Instance Mining for Gigapixel Histopathology Image Analysiscited by 1
  11. 2025RNN as Linear Transformer: A Closer Investigation into Representational Potentials of Visual Mamba Modelscited by 1
  12. 2025Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processingcited by 0
7 more
  1. 2025D2-Mamba: Dual-Scale Fusion and Dual-Path Scanning with SSMs for Shadow Removalcited by 0
  2. 2025TSkel-Mamba: Temporal Dynamic Modeling via State Space Model for Human Skeleton-based Action Recognitioncited by 0
  3. 2026How to Build Robust, Scalable Models for GSV-Based Indicators in Neighborhood Researchcited by 0
  4. 2026XtraLight-MedMamba for Classification of Neoplastic Tubular Adenomascited by 0
  5. 2026Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelinescited by 0
  6. 2026Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AIcited by 0
  7. 2026Learning Where to Focus: Self-Supervised Multi-Scale ViTs for Histopathologycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification
  2. 2022Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology
  3. 2024Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning
  4. 2025Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology
  5. 2024From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba
  6. 2024Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology
  7. 2024Boosting Vision-Language Models for Histopathology Classification: Predict all at once
  8. 2024MI-VisionShot: Few-shot adaptation of vision-language models for slide-level classification of histopathological images