mlsanityData to 14 Oct 2026

Structured State Space Models for Multiple Instance Learning in Digital Pathology

arXiv 2306.15789 ↗First posted 27 Jun 2023cs.CV cs.LGcited by 41

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

Abstract (arXiv)

Multiple instance learning is an ideal mode of analysis for histopathology data, where vast whole slide images are typically annotated with a single global label. In such cases, a whole slide image is modelled as a collection of tissue patches to be aggregated and classified. Common models for performing this classification include recurrent neural networks and transformers. Although powerful compression algorithms, such as deep pre-trained neural networks, are used to reduce the dimensionality of each patch, the sequences arising from whole slide images remain excessively long, routinely containing tens of thousands of patches. Structured state space models are an emerging alternative for sequence modelling, specifically designed for the efficient modelling of long sequences. These models invoke an optimal projection of an input sequence into memory units that compress the entire sequence. In this paper, we propose the use of state space models as a multiple instance learner to a variety of problems in digital pathology. Across experiments in metastasis detection, cancer subtyping, mutation classification, and multitask learning, we demonstrate the competitiveness of this new class of models with existing state of the art approaches. Our code is available at https://github.com/MICS-Lab/s4_digital_pathology.

Key facts. Cited by 41 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 15 of its 23 references that are in the map. First posted on arXiv 27 Jun 2023.

Builds on

15 of 23 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  5. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  6. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  7. 2016Language Modeling with Gated Convolutional Networkscited by 702
  8. 2021Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layerscited by 608
  9. 2020HiPPO: Recurrent Memory with Optimal Polynomial Projectionscited by 605
  10. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  11. 2022On the Parameterization and Initialization of Diagonal State Space Modelscited by 424
  12. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 359
3 more
  1. 2019Lookahead Optimizer: k steps forward, 1 step backcited by 336
  2. 2018Neural Image Compression for Gigapixel Histopathology Image Analysiscited by 65
  3. 2017Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Imagescited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 12; 2025: 11; 2026: 16.

41 papers.

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  1. 2024MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathologycited by 88
  2. 20242DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classificationcited by 35
  3. 2024Vim4Path: Self-Supervised Vision Mamba for Histopathology Imagescited by 19
  4. 2024xMIL: Insightful Explanations for Multiple Instance Learning in Histopathologycited by 16
  5. 2024HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classificationcited by 13
  6. 2024MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Modelscited by 12
  7. 2024Computational Pathology for Accurate Prediction of Breast Cancer Recurrence: Development and Validation of a Deep Learning-based Toolcited by 6
  8. 2025Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learningcited by 5
  9. 2024Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathologycited by 4
  10. 2024CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained imagescited by 3
  11. 2024Slide-based Graph Collaborative Training for Histopathology Whole Slide Image Analysiscited by 2
  12. 2025Efficient Multi-Slide Visual-Language Feature Fusion for Placental Disease Classificationcited by 2
29 more
  1. 2026Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathologycited by 2
  2. 2026Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Imagescited by 2
  3. 2023MixUp-MIL: A Study on Linear & Multilinear Interpolation-Based Data Augmentation for Whole Slide Image Classificationcited by 1
  4. 2024Learning Spatial-Preserving Hierarchical Representations for Digital Pathologycited by 1
  5. 2025LBMamba: Locally Bi-directional Mambacited by 1
  6. 2025MambaMIL+: Modeling Long-Term Contextual Patterns for Gigapixel Whole Slide Imagecited by 1
  7. 2026OCTOPUS: Enhancing the Spatial-Awareness of Vision SSMs with Multi-Dimensional Scans and Traversal Selectioncited by 1
  8. 2026Hierarchical Classification for Improved Histopathology Image Analysiscited by 1
  9. 2026PathMoE: Interpretable Multimodal Interaction Experts for Pediatric Brain Tumor Classificationcited by 1
  10. 2023Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classificationcited by 0
  11. 2024A Study on Context Length and Efficient Transformers for Biomedical Image Analysiscited by 0
  12. 2025Histomorphology-Guided Prototypical Multi-Instance Learning for Breast Cancer WSI Classificationcited by 0
  13. 2025SemaMIL: Semantic-Aware Multiple Instance Learning with Retrieval-Guided State Space Modeling for Whole Slide Imagescited by 0
  14. 2025SAC-MIL: Spatial-Aware Correlated Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 0
  15. 2025Positive Semi-definite Latent Factor Grouping-Boosted Cluster-reasoning Instance Disentangled Learning for WSI Representationcited by 0
  16. 2025StainNet: Scaling Self-Supervised Foundation Models on Immunohistochemistry and Special Stains for Computational Pathologycited by 0
  17. 2025DeltaMIL: Gated Memory Integration for Efficient and Discriminative Whole Slide Image Analysiscited by 0
  18. 2025WSD-MIL: Window Scale Decay Multiple Instance Learning for Whole Slide Image Classificationcited by 0
  19. 2026A Multi-scale Linear-time Encoder for Whole-Slide Image Analysiscited by 0
  20. 2026Prototype Instance-semantic Disentanglement with Low-rank Regularized Subspace Clustering for WSIs Explainable Recognitioncited by 0
  21. 2026MacNet: An End-to-End Manifold-Constrained Adaptive Clustering Network for Interpretable Whole Slide Image Classificationcited by 0
  22. 2026AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Imagecited by 0
  23. 2026ReconMIL: Synergizing Latent Space Reconstruction with Bi-Stream Mamba for Whole Slide Image Analysiscited by 0
  24. 2026SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classificationcited by 0
  25. 2026Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representationcited by 0
  26. 2026TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysiscited by 0
  27. 2026KHiM-Mamba: Injecting Pathology Knowledge into Mamba via Hidden-State Modulation for Whole Slide Image Analysiscited by 0
  28. 2026Test-Time Instance Selection for Improved Whole Slide Image Analysiscited by 0
  29. 2026MoSPR: Histology-to-Gene Expression Prediction with Morpho-Spatial Macrostates and Low-Rank Molecular Programscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning
  2. 2025PathRWKV: Enhancing Whole Slide Image Inference with Asymmetric Recurrent Modeling
  3. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification
  4. 2024Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification
  5. 2025Prototype-Based Multiple Instance Learning for Gigapixel Whole Slide Image Classification
  6. 2025Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology
  7. 2024M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysis
  8. 2025Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology