# Machine Learning Methods for Histopathological Image Analysis: A Review

arXiv: https://arxiv.org/abs/2102.03889 · HTML: https://mlsanity.com/paper/2102.03889/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 219 references that are in the map.
- First posted on arXiv 7 Feb 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [BACH: Grand Challenge on Breast Cancer Histology Images](https://mlsanity.com/paper/1808.04277/) (2018)
- [Machine learning methods for histopathological image analysis](https://mlsanity.com/paper/1709.00786/) (2017)
- [A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks](https://mlsanity.com/paper/2003.12255/) (2020)
- [The importance of stain normalization in colorectal tissue classification with convolutional networks](https://mlsanity.com/paper/1702.05931/) (2017)
- [Assessment of algorithms for mitosis detection in breast cancer histopathology images](https://mlsanity.com/paper/1411.5825/) (2014)
- [Automated Classification of Histopathology Images Using Transfer Learning](https://mlsanity.com/paper/1903.10035/) (2019)
- [Gland Instance Segmentation Using Deep Multichannel Neural Networks](https://mlsanity.com/paper/1611.06661/) (2016)
- [Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks](https://mlsanity.com/paper/1909.11870/) (2019)
- [Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images](https://mlsanity.com/paper/1704.00406/) (2017)
- [Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images](https://mlsanity.com/paper/1702.05803/) (2017)
- [Data Augmentation for Histopathological Images Based on Gaussian-Laplacian Pyramid Blending](https://mlsanity.com/paper/2002.00072/) (2020)
- [Double Transfer Learning for Breast Cancer Histopathologic Image Classification](https://mlsanity.com/paper/1904.07834/) (2019)
- [Texture CNN for Histopathological Image Classification](https://mlsanity.com/paper/1905.12005/) (2019)

## Built on by (most cited first, 10 of 10)

- [BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images](https://mlsanity.com/paper/2111.04740/) (2021)
- [Self-Supervised Representation Learning for Nerve Fiber Distribution Patterns in 3D-PLI](https://mlsanity.com/paper/2401.17207/) (2024)
- [Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform](https://mlsanity.com/paper/2202.13270/) (2022)
- [Automatic Data Augmentation Learning using Bilevel Optimization for Histopathological Images](https://mlsanity.com/paper/2307.11808/) (2023)
- [Unmasking Biases and Reliability Concerns in Convolutional Neural Networks Analysis of Cancer Pathology Images](https://mlsanity.com/paper/2603.12445/) (2026)
- [Multiscale Analysis for Improving Texture Classification](https://mlsanity.com/paper/2204.09841/) (2022)
- [Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning](https://mlsanity.com/paper/2406.14351/) (2024)
- [Deep Learning for Classification of Inflammatory Bowel Disease Activity in Whole Slide Images of Colonic Histopathology](https://mlsanity.com/paper/2410.19690/) (2024)
- [CREATE-FFPE: Cross-Resolution Compensated and Multi-Frequency Enhanced FS-to-FFPE Stain Transfer for Intraoperative IHC Images](https://mlsanity.com/paper/2503.00697/) (2025)
- [Challenges in Deep Learning-Based Small Organ Segmentation: A Benchmarking Perspective for Medical Research with Limited Datasets](https://mlsanity.com/paper/2509.05892/) (2025)

## Abstract (arXiv)

Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is not only time and resource consuming, but also very challenging even for experienced pathologists, resulting in inter- and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. In this paper, we present a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. In addition, we present a list of publicly available and private datasets that have been used in HI research.