# Benchmarking Pathology Feature Extractors for Whole Slide Image Classification

arXiv: https://arxiv.org/abs/2311.11772 · HTML: https://mlsanity.com/paper/2311.11772/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2023.

## Built on by (most cited first, 9 of 9)

- [Distilling foundation models for robust and efficient models in digital pathology](https://mlsanity.com/paper/2501.16239/) (2025)
- [Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning](https://mlsanity.com/paper/2411.13623/) (2024)
- [A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification](https://mlsanity.com/paper/2405.09990/) (2024)
- [A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models](https://mlsanity.com/paper/2408.14496/) (2024)
- [Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts](https://mlsanity.com/paper/2410.06723/) (2024)
- [Controllable Latent Space Augmentation for Digital Pathology](https://mlsanity.com/paper/2508.14588/) (2025)
- [Comparing Computational Pathology Foundation Models using Representational Similarity Analysis](https://mlsanity.com/paper/2509.15482/) (2025)
- [MB-DSMIL-CL-PL: Scalable Weakly Supervised Ovarian Cancer Subtype Classification and Localisation Using Contrastive and Prototype Learning with Frozen Patch Features](https://mlsanity.com/paper/2602.15138/) (2026)
- [From Patches to Patients: A study of the tile-to-slide performance transferability in Digital Pathology](https://mlsanity.com/paper/2606.10778/) (2026)

## Abstract (arXiv)

Weakly supervised whole slide image classification is a key task in computational pathology, which involves predicting a slide-level label from a set of image patches constituting the slide. Constructing models to solve this task involves multiple design choices, often made without robust empirical or conclusive theoretical justification. To address this, we conduct a comprehensive benchmarking of feature extractors to answer three critical questions: 1) Is stain normalisation still a necessary preprocessing step? 2) Which feature extractors are best for downstream slide-level classification? 3) How does magnification affect downstream performance? Our study constitutes the most comprehensive evaluation of publicly available pathology feature extractors to date, involving more than 10,000 training runs across 14 feature extractors, 9 tasks, 5 datasets, 3 downstream architectures, 2 levels of magnification, and various preprocessing setups. Our findings challenge existing assumptions: 1) We observe empirically, and by analysing the latent space, that skipping stain normalisation and image augmentations does not degrade performance, while significantly reducing memory and computational demands. 2) We develop a novel evaluation metric to compare relative downstream performance, and show that the choice of feature extractor is the most consequential factor for downstream performance. 3) We find that lower-magnification slides are sufficient for accurate slide-level classification. Contrary to previous patch-level benchmarking studies, our approach emphasises clinical relevance by focusing on slide-level biomarker prediction tasks in a weakly supervised setting with external validation cohorts. Our findings stand to streamline digital pathology workflows by minimising preprocessing needs and informing the selection of feature extractors.