# Improving Representation Learning for Histopathologic Images with Cluster Constraints

arXiv: https://arxiv.org/abs/2310.12334 · HTML: https://mlsanity.com/paper/2310.12334/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 46 references that are in the map.
- First posted on arXiv 18 Oct 2023.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [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)
- [SCAN: Learning to Classify Images without Labels](https://mlsanity.com/paper/2005.12320/) (2020)
- [With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2104.14548/) (2021)
- [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)
- [Deep Learning for Identifying Metastatic Breast Cancer](https://mlsanity.com/paper/1606.05718/) (2016)
- [Detecting Cancer Metastases on Gigapixel Pathology Images](https://mlsanity.com/paper/1703.02442/) (2017)
- [Weakly supervised multiple instance learning histopathological tumor segmentation](https://mlsanity.com/paper/2004.05024/) (2020)
- [Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks](https://mlsanity.com/paper/1901.11489/) (2019)
- [Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations](https://mlsanity.com/paper/2106.13689/) (2021)
- [Self-similarity Student for Partial Label Histopathology Image Segmentation](https://mlsanity.com/paper/2007.09610/) (2020)
- [ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images](https://mlsanity.com/paper/2207.06733/) (2022)
- [Resolution-Based Distillation for Efficient Histology Image Classification](https://mlsanity.com/paper/2101.04170/) (2021)
- [Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2209.09433/) (2022)
- [Unsupervised Representation Learning from Pathology Images with Multi-directional Contrastive Predictive Coding](https://mlsanity.com/paper/2105.05345/) (2021)
- [HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning](https://mlsanity.com/paper/2209.06185/) (2022)

## Built on by (most cited first, 8 of 8)

- [Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Study](https://mlsanity.com/paper/2505.01109/) (2025)
- [Learning Spatial-Preserving Hierarchical Representations for Digital Pathology](https://mlsanity.com/paper/2406.09333/) (2024)
- [Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnostics](https://mlsanity.com/paper/2411.05959/) (2024)
- [Exploiting Label-Independent Regularization from Spatial Dependencies for Whole Slide Image Analysis](https://mlsanity.com/paper/2602.19487/) (2026)
- [HiST: A Hierarchical Sparse Transformer for Cross-Modal Spatial Transcriptomics Modeling](https://mlsanity.com/paper/2606.14251/) (2026)
- [Leveraging Spatial Context for Positive Pair Sampling in Histopathology Image Representation Learning](https://mlsanity.com/paper/2503.05170/) (2025)
- [Prototype Instance-semantic Disentanglement with Low-rank Regularized Subspace Clustering for WSIs Explainable Recognition](https://mlsanity.com/paper/2602.14501/) (2026)
- [MacNet: An End-to-End Manifold-Constrained Adaptive Clustering Network for Interpretable Whole Slide Image Classification](https://mlsanity.com/paper/2602.14509/) (2026)

## Abstract (arXiv)

Recent advances in whole-slide image (WSI) scanners and computational capabilities have significantly propelled the application of artificial intelligence in histopathology slide analysis. While these strides are promising, current supervised learning approaches for WSI analysis come with the challenge of exhaustively labeling high-resolution slides - a process that is both labor-intensive and time-consuming. In contrast, self-supervised learning (SSL) pretraining strategies are emerging as a viable alternative, given that they don't rely on explicit data annotations. These SSL strategies are quickly bridging the performance disparity with their supervised counterparts. In this context, we introduce an SSL framework. This framework aims for transferable representation learning and semantically meaningful clustering by synergizing invariance loss and clustering loss in WSI analysis. Notably, our approach outperforms common SSL methods in downstream classification and clustering tasks, as evidenced by tests on the Camelyon16 and a pancreatic cancer dataset.