# A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches

arXiv: https://arxiv.org/abs/2102.10553 · HTML: https://mlsanity.com/paper/2102.10553/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 295 references that are in the map.
- First posted on arXiv 21 Feb 2021.

## Builds on (most cited first, 30 of 32)

- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification](https://mlsanity.com/paper/1504.07947/) (2015)
- [Deep Learning for Identifying Metastatic Breast Cancer](https://mlsanity.com/paper/1606.05718/) (2016)
- [Machine learning methods for histopathological image analysis](https://mlsanity.com/paper/1709.00786/) (2017)
- [Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge](https://mlsanity.com/paper/1807.08284/) (2018)
- [Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks](https://mlsanity.com/paper/1808.05896/) (2018)
- [Deep Learning for Whole Slide Image Analysis: An Overview](https://mlsanity.com/paper/1910.11097/) (2019)
- [Domain-adversarial neural networks to address the appearance variability of histopathology images](https://mlsanity.com/paper/1707.06183/) (2017)
- [Simulation of hyperelastic materials in real-time using Deep Learning](https://mlsanity.com/paper/1904.06197/) (2019)
- [Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach](https://mlsanity.com/paper/1802.02212/) (2018)
- [DeepDownscale: a Deep Learning Strategy for High-Resolution Weather Forecast](https://mlsanity.com/paper/1808.05264/) (2018)
- [RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification](https://mlsanity.com/paper/2010.06440/) (2020)
- [Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images](https://mlsanity.com/paper/1703.01550/) (2017)
- [Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images](https://mlsanity.com/paper/1806.01357/) (2018)
- [Assessment of algorithms for mitosis detection in breast cancer histopathology images](https://mlsanity.com/paper/1411.5825/) (2014)
- [Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology](https://mlsanity.com/paper/1805.06983/) (2018)
- [CAGNet: Content-Aware Guidance for Salient Object Detection](https://mlsanity.com/paper/1911.13168/) (2019)
- [Gastric histopathology image segmentation using a hierarchical conditional random field](https://mlsanity.com/paper/2003.01302/) (2020)
- [A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images](https://mlsanity.com/paper/1803.02786/) (2018)
- [ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images](https://mlsanity.com/paper/1707.09597/) (2017)
- [Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection](https://mlsanity.com/paper/1901.04619/) (2019)
- [Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images](https://mlsanity.com/paper/1807.11113/) (2018)
- [Deep Barcodes for Fast Retrieval of Histopathology Scans](https://mlsanity.com/paper/1805.08833/) (2018)
- [Improving Whole Slide Segmentation Through Visual Context - A Systematic Study](https://mlsanity.com/paper/1806.04259/) (2018)
- [A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation](https://mlsanity.com/paper/2003.03744/) (2020)
- [Learning to Segment Breast Biopsy Whole Slide Images](https://mlsanity.com/paper/1709.02554/) (2017)
- [Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images](https://mlsanity.com/paper/1705.03678/) (2017)
- [An attention-based multi-resolution model for prostate whole slide imageclassification and localization](https://mlsanity.com/paper/1905.13208/) (2019)
- [Classification and Retrieval of Digital Pathology Scans: A New Dataset](https://mlsanity.com/paper/1705.07522/) (2017)

## Built on by (most cited first, 30 of 44)

- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification](https://mlsanity.com/paper/2210.03664/) (2022)
- [DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets](https://mlsanity.com/paper/2306.14132/) (2023)
- [Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2301.08125/) (2023)
- [FICE: Text-Conditioned Fashion Image Editing With Guided GAN Inversion](https://mlsanity.com/paper/2301.02110/) (2023)
- [EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks](https://mlsanity.com/paper/2212.00532/) (2022)
- [Rotation-Agnostic Image Representation Learning for Digital Pathology](https://mlsanity.com/paper/2311.08359/) (2023)
- [A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches](https://mlsanity.com/paper/2105.03148/) (2021)
- [Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners](https://mlsanity.com/paper/2205.09048/) (2022)
- [PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images](https://mlsanity.com/paper/2503.17970/) (2025)
- [Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images](https://mlsanity.com/paper/2212.12844/) (2022)
- [A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers](https://mlsanity.com/paper/2104.06243/) (2021)
- [Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology](https://mlsanity.com/paper/2204.08311/) (2022)
- [GradMix for nuclei segmentation and classification in imbalanced pathology image datasets](https://mlsanity.com/paper/2210.12938/) (2022)
- [M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysis](https://mlsanity.com/paper/2407.17267/) (2024)
- [Deep Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks](https://mlsanity.com/paper/2207.07173/) (2022)
- [Breast Cancer Histopathology Image based Gene Expression Prediction using Spatial Transcriptomics data and Deep Learning](https://mlsanity.com/paper/2303.09987/) (2023)
- [DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images](https://mlsanity.com/paper/2410.03058/) (2024)
- [Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2206.10878/) (2022)
- [Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image](https://mlsanity.com/paper/2411.10709/) (2024)
- [EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation](https://mlsanity.com/paper/2202.08552/) (2022)
- [Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review](https://mlsanity.com/paper/2208.12460/) (2022)
- [FALFormer: Feature-aware Landmarks self-attention for Whole-slide Image Classification](https://mlsanity.com/paper/2407.07340/) (2024)
- [EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation](https://mlsanity.com/paper/2110.07723/) (2021)
- [Product Re-identification System in Fully Automated Defect Detection](https://mlsanity.com/paper/2112.10324/) (2021)
- [TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos](https://mlsanity.com/paper/2204.08166/) (2022)
- [A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer](https://mlsanity.com/paper/2205.12843/) (2022)
- [PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage](https://mlsanity.com/paper/2308.10521/) (2023)
- [MECFormer: Multi-task Whole Slide Image Classification with Expert Consultation Network](https://mlsanity.com/paper/2410.04507/) (2024)
- [HyperPath: Knowledge-Guided Hyperbolic Semantic Hierarchy Modeling for WSI Analysis](https://mlsanity.com/paper/2506.16398/) (2025)

## Abstract (arXiv)

With the development of computer-aided diagnosis (CAD) and image scanning technology, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital pathology. Since 2004, WSI has been used more and more in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computers, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists obtain more stable and quantitative analysis results, save labor costs and improve diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning in WSI segmentation, classification, and detection are reviewed continuously. Finally, the existing methods are studied, the applicabilities of the analysis methods are analyzed, and the application prospects of the analysis methods in this field are forecasted.