# EvoPS: Evolutionary Patch Selection for Whole Slide Image Analysis in Computational Pathology

arXiv: https://arxiv.org/abs/2511.07560 · HTML: https://mlsanity.com/paper/2511.07560/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 10 of 10)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Differentiable Patch Selection for Image Recognition](https://mlsanity.com/paper/2104.03059/) (2021)
- [Yottixel -- An Image Search Engine for Large Archives of Histopathology Whole Slide Images](https://mlsanity.com/paper/1911.08748/) (2019)
- [PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers](https://mlsanity.com/paper/2203.11987/) (2022)
- [Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Images](https://mlsanity.com/paper/2302.09682/) (2023)
- [Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images](https://mlsanity.com/paper/2303.00943/) (2023)
- [Trusted Multi-Scale Classification Framework for Whole Slide Image](https://mlsanity.com/paper/2207.05290/) (2022)
- [Creating an Atlas of Normal Tissue for Pruning WSI Patching Through Anomaly Detection](https://mlsanity.com/paper/2310.03106/) (2023)
- [Minimal High-Resolution Patches Are Sufficient for Whole Slide Image Representation via Cascaded Dual-Scale Reconstruction](https://mlsanity.com/paper/2508.01641/) (2025)

## Built on by (most cited first, 1 of 1)

- [InfoDPP-PAC: Principled Patch Selection for Whole Slide Image Analysis](https://mlsanity.com/paper/2608.23574/) (2026)

## Abstract (arXiv)

In computational pathology, the gigapixel scale of Whole-Slide Images (WSIs) necessitates their division into thousands of smaller patches. Analyzing these high-dimensional patch embeddings is computationally expensive and risks diluting key diagnostic signals with many uninformative patches. Existing patch selection methods often rely on random sampling or simple clustering heuristics and typically fail to explicitly manage the crucial trade-off between the number of selected patches and the accuracy of the resulting slide representation. To address this gap, we propose EvoPS (Evolutionary Patch Selection), a novel framework that formulates patch selection as a multi-objective optimization problem and leverages an evolutionary search to simultaneously minimize the number of selected patch embeddings and maximize the performance of a downstream similarity search task, generating a Pareto front of optimal trade-off solutions. We validated our framework across four major cancer cohorts from The Cancer Genome Atlas (TCGA) using five pretrained deep learning models to generate patch embeddings, including both supervised CNNs and large self-supervised foundation models. The results demonstrate that EvoPS can reduce the required number of training patch embeddings by over 90% while consistently maintaining or even improving the final classification F1-score compared to a baseline that uses all available patches' embeddings selected through a standard extraction pipeline. The EvoPS framework provides a robust and principled method for creating efficient, accurate, and interpretable WSI representations, empowering users to select an optimal balance between computational cost and diagnostic performance.