# Machine learning methods for histopathological image analysis

arXiv: https://arxiv.org/abs/1709.00786 · HTML: https://mlsanity.com/paper/1709.00786/

## Key facts

- Cited by 79 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2017.

## Built on by (most cited first, 30 of 79)

- [Extending the WILDS Benchmark for Unsupervised Adaptation](https://mlsanity.com/paper/2112.05090/) (2021)
- [Hierarchical Graph Representations in Digital Pathology](https://mlsanity.com/paper/2102.11057/) (2021)
- [CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images](https://mlsanity.com/paper/2101.00442/) (2021)
- [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)
- [Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization](https://mlsanity.com/paper/2206.07837/) (2022)
- [Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation](https://mlsanity.com/paper/2102.01678/) (2021)
- [SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology](https://mlsanity.com/paper/2307.09570/) (2023)
- [FHIST: A Benchmark for Few-shot Classification of Histological Images](https://mlsanity.com/paper/2206.00092/) (2022)
- [Self-learning for weakly supervised Gleason grading of local patterns](https://mlsanity.com/paper/2105.10420/) (2021)
- [The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images](https://mlsanity.com/paper/2401.08865/) (2024)
- [Multi stain graph fusion for multimodal integration in pathology](https://mlsanity.com/paper/2204.12541/) (2022)
- [Machine Learning Methods for Histopathological Image Analysis: A Review](https://mlsanity.com/paper/2102.03889/) (2021)
- [WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images](https://mlsanity.com/paper/2105.10445/) (2021)
- [Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection](https://mlsanity.com/paper/2108.09178/) (2021)
- [Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images](https://mlsanity.com/paper/2203.07707/) (2022)
- [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)
- [Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology](https://mlsanity.com/paper/2404.19113/) (2024)
- [HistoViT: Vision Transformer for Accurate and Scalable Histopathological Cancer Diagnosis](https://mlsanity.com/paper/2508.11181/) (2025)
- [Negative Evidence Matters in Interpretable Histology Image Classification](https://mlsanity.com/paper/2201.02445/) (2022)
- [Reconstruction, forecasting, and stability of chaotic dynamics from partial data](https://mlsanity.com/paper/2305.15111/) (2023)
- [Boosting Vision-Language Models for Histopathology Classification: Predict all at once](https://mlsanity.com/paper/2409.01883/) (2024)
- [Generative Classifiers Avoid Shortcut Solutions](https://mlsanity.com/paper/2512.25034/) (2025)
- [Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps](https://mlsanity.com/paper/2103.12155/) (2021)
- [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)
- [Multi-scale Deep Learning Architecture for Nucleus Detection in Renal Cell Carcinoma Microscopy Image](https://mlsanity.com/paper/2104.13557/) (2021)
- [Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation](https://mlsanity.com/paper/2203.15015/) (2022)
- [Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology](https://mlsanity.com/paper/2303.07771/) (2023)
- [Generalizing to Unseen Domains in Diabetic Retinopathy Classification](https://mlsanity.com/paper/2310.17255/) (2023)
- [MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification](https://mlsanity.com/paper/2408.12825/) (2024)
- [Rethinking Machine Learning Model Evaluation in Pathology](https://mlsanity.com/paper/2204.05205/) (2022)

## Abstract (arXiv)

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.