# Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology

arXiv: https://arxiv.org/abs/2405.11643 · HTML: https://mlsanity.com/paper/2405.11643/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 100 references that are in the map.
- First posted on arXiv 19 May 2024.

## Builds on (most cited first, 30 of 41)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks](https://mlsanity.com/paper/2009.11169/) (2020)
- [Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification](https://mlsanity.com/paper/1504.07947/) (2015)
- [Benchmarking Self-Supervised Learning on Diverse Pathology Datasets](https://mlsanity.com/paper/2212.04690/) (2022)
- [Self supervised contrastive learning for digital histopathology](https://mlsanity.com/paper/2011.13971/) (2020)
- [Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images](https://mlsanity.com/paper/2306.07831/) (2023)
- [Artificial Intelligence for Digital and Computational Pathology](https://mlsanity.com/paper/2401.06148/) (2023)
- [Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction](https://mlsanity.com/paper/2304.06819/) (2023)
- [Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images](https://mlsanity.com/paper/2303.06873/) (2023)
- [Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction](https://mlsanity.com/paper/2306.08330/) (2023)
- [Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images](https://mlsanity.com/paper/2001.01599/) (2020)
- [Automated Gleason Grading of Prostate Biopsies using Deep Learning](https://mlsanity.com/paper/1907.07980/) (2019)
- [Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification](https://mlsanity.com/paper/2307.15254/) (2023)
- [Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology](https://mlsanity.com/paper/2206.01794/) (2022)
- [Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images](https://mlsanity.com/paper/2204.12454/) (2022)
- [Yottixel -- An Image Search Engine for Large Archives of Histopathology Whole Slide Images](https://mlsanity.com/paper/1911.08748/) (2019)
- [Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Images](https://mlsanity.com/paper/2202.07001/) (2022)
- [Giga-SSL: Self-Supervised Learning for Gigapixel Images](https://mlsanity.com/paper/2212.03273/) (2022)

## Built on by (most cited first, 30 of 72)

- [Multimodal Prototyping for cancer survival prediction](https://mlsanity.com/paper/2407.00224/) (2024)
- [PathAlign: A vision-language model for whole slide images in histopathology](https://mlsanity.com/paper/2406.19578/) (2024)
- [Do Multiple Instance Learning Models Transfer?](https://mlsanity.com/paper/2506.09022/) (2025)
- [Multistain Pretraining for Slide Representation Learning in Pathology](https://mlsanity.com/paper/2408.02859/) (2024)
- [FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification](https://mlsanity.com/paper/2411.14743/) (2024)
- [Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology](https://mlsanity.com/paper/2409.09369/) (2024)
- [Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification](https://mlsanity.com/paper/2410.10573/) (2024)
- [GECKO: Gigapixel Vision-Concept Contrastive Pretraining in Histopathology](https://mlsanity.com/paper/2504.01009/) (2025)
- [OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport](https://mlsanity.com/paper/2506.20741/) (2025)
- [Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation](https://mlsanity.com/paper/2504.18856/) (2025)
- [DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction](https://mlsanity.com/paper/2510.00053/) (2025)
- [ASMIL: Attention-Stabilized Multiple Instance Learning for Whole Slide Imaging](https://mlsanity.com/paper/2603.06658/) (2026)
- [SurvAgent: Hierarchical CoT-Enhanced Case Banking and Dichotomy-Based Multi-Agent System for Multimodal Survival Prediction](https://mlsanity.com/paper/2511.16635/) (2025)
- [Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology](https://mlsanity.com/paper/2408.15032/) (2024)
- [Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image](https://mlsanity.com/paper/2411.10709/) (2024)
- [Disentangled and Interpretable Multimodal Attention Fusion for Cancer Survival Prediction](https://mlsanity.com/paper/2503.16069/) (2025)
- [QCAgent: An agentic framework for quality-controllable pathology report generation from whole slide image](https://mlsanity.com/paper/2603.01647/) (2026)
- [Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning](https://mlsanity.com/paper/2603.22198/) (2026)
- [This looks like what? Challenges and Future Research Directions for Part-Prototype Models](https://mlsanity.com/paper/2502.09340/) (2025)
- [Can We Simplify Slide-level Fine-tuning of Pathology Foundation Models?](https://mlsanity.com/paper/2502.20823/) (2025)
- [PathVQ: Reforming Computational Pathology Foundation Model for Whole Slide Image Analysis via Vector Quantization](https://mlsanity.com/paper/2503.06482/) (2025)
- [Agglomerating Large Vision Encoders via Distillation for VFSS Segmentation](https://mlsanity.com/paper/2504.02351/) (2025)
- [Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis](https://mlsanity.com/paper/2507.08178/) (2025)
- [PTCMIL: Multiple Instance Learning via Prompt Token Clustering for Whole Slide Image Analysis](https://mlsanity.com/paper/2507.18848/) (2025)
- [PS3: A Multimodal Transformer Integrating Pathology Reports with Histology Images and Biological Pathways for Cancer Survival Prediction](https://mlsanity.com/paper/2509.20022/) (2025)
- [Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning](https://mlsanity.com/paper/2407.14904/) (2024)
- [Slide-based Graph Collaborative Training for Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2410.10260/) (2024)
- [From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer](https://mlsanity.com/paper/2412.16715/) (2024)
- [HySurvPred: Multimodal Hyperbolic Embedding with Angle-Aware Hierarchical Contrastive Learning and Uncertainty Constraints for Survival Prediction](https://mlsanity.com/paper/2503.13862/) (2025)
- [Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data](https://mlsanity.com/paper/2504.12351/) (2025)

## Abstract (arXiv)

Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL). However, the slide representations resulting from this approach are highly tailored to specific clinical tasks, which limits their expressivity and generalization, particularly in scenarios with limited data. Instead, we hypothesize that morphological redundancy in tissue can be leveraged to build a task-agnostic slide representation in an unsupervised fashion. To this end, we introduce PANTHER, a prototype-based approach rooted in the Gaussian mixture model that summarizes the set of WSI patches into a much smaller set of morphological prototypes. Specifically, each patch is assumed to have been generated from a mixture distribution, where each mixture component represents a morphological exemplar. Utilizing the estimated mixture parameters, we then construct a compact slide representation that can be readily used for a wide range of downstream tasks. By performing an extensive evaluation of PANTHER on subtyping and survival tasks using 13 datasets, we show that 1) PANTHER outperforms or is on par with supervised MIL baselines and 2) the analysis of morphological prototypes brings new qualitative and quantitative insights into model interpretability.