# Structured State Space Models for Multiple Instance Learning in Digital Pathology

arXiv: https://arxiv.org/abs/2306.15789 · HTML: https://mlsanity.com/paper/2306.15789/

## 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.

## Placed in

- State-space sequence models (S4, Mamba) / [Mamba for medical image analysis](https://mlsanity.com/ssm/mamba-for-medical-image-analysis/)

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021)
- [HiPPO: Recurrent Memory with Optimal Polynomial Projections](https://mlsanity.com/paper/2008.07669/) (2020)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [On the Parameterization and Initialization of Diagonal State Space Models](https://mlsanity.com/paper/2206.11893/) (2022)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Lookahead Optimizer: k steps forward, 1 step back](https://mlsanity.com/paper/1907.08610/) (2019)
- [Neural Image Compression for Gigapixel Histopathology Image Analysis](https://mlsanity.com/paper/1811.02840/) (2018)
- [Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images](https://mlsanity.com/paper/1707.08814/) (2017)

## Built on by (most cited first, 30 of 41)

- [MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology](https://mlsanity.com/paper/2403.06800/) (2024)
- [2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification](https://mlsanity.com/paper/2412.00678/) (2024)
- [Vim4Path: Self-Supervised Vision Mamba for Histopathology Images](https://mlsanity.com/paper/2404.13222/) (2024)
- [xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology](https://mlsanity.com/paper/2406.04280/) (2024)
- [HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification](https://mlsanity.com/paper/2411.07660/) (2024)
- [MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models](https://mlsanity.com/paper/2403.05160/) (2024)
- [Computational Pathology for Accurate Prediction of Breast Cancer Recurrence: Development and Validation of a Deep Learning-based Tool](https://mlsanity.com/paper/2409.15491/) (2024)
- [Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learning](https://mlsanity.com/paper/2502.21130/) (2025)
- [Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology](https://mlsanity.com/paper/2408.15032/) (2024)
- [CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained images](https://mlsanity.com/paper/2401.16131/) (2024)
- [Slide-based Graph Collaborative Training for Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2410.10260/) (2024)
- [Efficient Multi-Slide Visual-Language Feature Fusion for Placental Disease Classification](https://mlsanity.com/paper/2508.03277/) (2025)
- [Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology](https://mlsanity.com/paper/2603.08328/) (2026)
- [Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images](https://mlsanity.com/paper/2603.18461/) (2026)
- [MixUp-MIL: A Study on Linear & Multilinear Interpolation-Based Data Augmentation for Whole Slide Image Classification](https://mlsanity.com/paper/2311.03052/) (2023)
- [Learning Spatial-Preserving Hierarchical Representations for Digital Pathology](https://mlsanity.com/paper/2406.09333/) (2024)
- [LBMamba: Locally Bi-directional Mamba](https://mlsanity.com/paper/2506.15976/) (2025)
- [MambaMIL+: Modeling Long-Term Contextual Patterns for Gigapixel Whole Slide Image](https://mlsanity.com/paper/2512.17726/) (2025)
- [OCTOPUS: Enhancing the Spatial-Awareness of Vision SSMs with Multi-Dimensional Scans and Traversal Selection](https://mlsanity.com/paper/2602.00904/) (2026)
- [Hierarchical Classification for Improved Histopathology Image Analysis](https://mlsanity.com/paper/2603.00504/) (2026)
- [PathMoE: Interpretable Multimodal Interaction Experts for Pediatric Brain Tumor Classification](https://mlsanity.com/paper/2603.01547/) (2026)
- [Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classification](https://mlsanity.com/paper/2310.07464/) (2023)
- [A Study on Context Length and Efficient Transformers for Biomedical Image Analysis](https://mlsanity.com/paper/2501.00619/) (2024)
- [Histomorphology-Guided Prototypical Multi-Instance Learning for Breast Cancer WSI Classification](https://mlsanity.com/paper/2503.17983/) (2025)
- [SemaMIL: Semantic-Aware Multiple Instance Learning with Retrieval-Guided State Space Modeling for Whole Slide Images](https://mlsanity.com/paper/2509.00442/) (2025)
- [SAC-MIL: Spatial-Aware Correlated Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2509.03973/) (2025)
- [Positive Semi-definite Latent Factor Grouping-Boosted Cluster-reasoning Instance Disentangled Learning for WSI Representation](https://mlsanity.com/paper/2511.01304/) (2025)
- [StainNet: Scaling Self-Supervised Foundation Models on Immunohistochemistry and Special Stains for Computational Pathology](https://mlsanity.com/paper/2512.10326/) (2025)
- [DeltaMIL: Gated Memory Integration for Efficient and Discriminative Whole Slide Image Analysis](https://mlsanity.com/paper/2512.19331/) (2025)
- [WSD-MIL: Window Scale Decay Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2512.19982/) (2025)

## 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.