# Deep Learning for Whole Slide Image Analysis: An Overview

arXiv: https://arxiv.org/abs/1910.11097 · HTML: https://mlsanity.com/paper/1910.11097/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 30 of 51)

- [A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches](https://mlsanity.com/paper/2102.10553/) (2021)
- [Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2301.08125/) (2023)
- [Towards Large-Scale Training of Pathology Foundation Models](https://mlsanity.com/paper/2404.15217/) (2024)
- [FHIST: A Benchmark for Few-shot Classification of Histological Images](https://mlsanity.com/paper/2206.00092/) (2022)
- [Gigapixel Whole-Slide Images Classification using Locally Supervised Learning](https://mlsanity.com/paper/2207.08267/) (2022)
- [Masked Pre-Training of Transformers for Histology Image Analysis](https://mlsanity.com/paper/2304.07434/) (2023)
- [Cross-scale Multi-instance Learning for Pathological Image Diagnosis](https://mlsanity.com/paper/2304.00216/) (2023)
- [From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology](https://mlsanity.com/paper/2204.05044/) (2022)
- [Efficient High-Resolution Deep Learning: A Survey](https://mlsanity.com/paper/2207.13050/) (2022)
- [Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search](https://mlsanity.com/paper/2208.13653/) (2022)
- [PATHS: A Hierarchical Transformer for Efficient Whole Slide Image Analysis](https://mlsanity.com/paper/2411.18225/) (2024)
- [DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction](https://mlsanity.com/paper/2510.00053/) (2025)
- [Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images](https://mlsanity.com/paper/2106.06623/) (2021)
- [Stain Based Contrastive Co-training for Histopathological Image Analysis](https://mlsanity.com/paper/2206.12505/) (2022)
- [Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Images](https://mlsanity.com/paper/2302.09682/) (2023)
- [TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2305.00696/) (2023)
- [Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2206.10878/) (2022)
- [Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images](https://mlsanity.com/paper/2208.07322/) (2022)
- [Creating an Atlas of Normal Tissue for Pruning WSI Patching Through Anomaly Detection](https://mlsanity.com/paper/2310.03106/) (2023)
- [A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer](https://mlsanity.com/paper/2311.17740/) (2023)
- [Focused Active Learning for Histopathological Image Classification](https://mlsanity.com/paper/2404.04663/) (2024)
- [Pan-cancer Histopathology WSI Pre-training with Position-aware Masked Autoencoder](https://mlsanity.com/paper/2407.07504/) (2024)
- [IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in Histopathology](https://mlsanity.com/paper/2408.16395/) (2024)
- [Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits](https://mlsanity.com/paper/2112.09496/) (2021)
- [Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification](https://mlsanity.com/paper/2204.10942/) (2022)
- [IHC Matters: Incorporating IHC analysis to H&E Whole Slide Image Analysis for Improved Cancer Grading via Two-stage Multimodal Bilinear Pooling Fusion](https://mlsanity.com/paper/2405.08197/) (2024)
- [Fine-grained Multi-class Nuclei Segmentation with Molecular-empowered All-in-SAM Model](https://mlsanity.com/paper/2508.15751/) (2025)
- [PIMIP: An Open Source Platform for Pathology Information Management and Integration](https://mlsanity.com/paper/2111.05794/) (2021)
- [Magnifying Networks for Images with Billions of Pixels](https://mlsanity.com/paper/2112.06121/) (2021)
- [Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia](https://mlsanity.com/paper/2201.01449/) (2022)

## Abstract (arXiv)

The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artefacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.