# A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

arXiv: https://arxiv.org/abs/2003.12255 · HTML: https://mlsanity.com/paper/2003.12255/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

## Built on by (most cited first, 25 of 25)

- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (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)
- [Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer](https://mlsanity.com/paper/2108.00358/) (2021)
- [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)
- [Machine Learning Methods for Histopathological Image Analysis: A Review](https://mlsanity.com/paper/2102.03889/) (2021)
- [hist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images](https://mlsanity.com/paper/2304.04507/) (2023)
- [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)
- [Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications](https://mlsanity.com/paper/2202.08916/) (2022)
- [IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach](https://mlsanity.com/paper/2206.03368/) (2022)
- [Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology](https://mlsanity.com/paper/2204.08311/) (2022)
- [SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2311.00048/) (2023)
- [Breast cancer detection using artificial intelligence techniques: A systematic literature review](https://mlsanity.com/paper/2203.04308/) (2022)
- [Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Images](https://mlsanity.com/paper/2302.09682/) (2023)
- [Breast Cancer Histopathology Image based Gene Expression Prediction using Spatial Transcriptomics data and Deep Learning](https://mlsanity.com/paper/2303.09987/) (2023)
- [EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation](https://mlsanity.com/paper/2202.08552/) (2022)
- [A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer](https://mlsanity.com/paper/2205.12843/) (2022)
- [Stain Normalized Breast Histopathology Image Recognition using Convolutional Neural Networks for Cancer Detection](https://mlsanity.com/paper/2201.00957/) (2022)
- [What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2201.08550/) (2022)
- [A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements](https://mlsanity.com/paper/2202.09020/) (2022)
- [An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images](https://mlsanity.com/paper/2204.01341/) (2022)
- [A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images](https://mlsanity.com/paper/2304.05022/) (2023)
- [A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification](https://mlsanity.com/paper/2202.06465/) (2022)
- [Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer](https://mlsanity.com/paper/2205.08467/) (2022)
- [XtraLight-MedMamba for Classification of Neoplastic Tubular Adenomas](https://mlsanity.com/paper/2602.04819/) (2026)
- [MagViT: Interpretable Multi-Magnification Transformers with Patient-Level Model Selection for Breast Histopathology](https://mlsanity.com/paper/2608.16959/) (2026)

## Abstract (arXiv)

Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.