# Automated Classification of Histopathology Images Using Transfer Learning

arXiv: https://arxiv.org/abs/1903.10035 · HTML: https://mlsanity.com/paper/1903.10035/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2019.

## Built on by (most cited first, 14 of 14)

- [A comparative study between vision transformers and CNNs in digital pathology](https://mlsanity.com/paper/2206.00389/) (2022)
- [Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs](https://mlsanity.com/paper/2403.07743/) (2024)
- [Machine Learning Methods for Histopathological Image Analysis: A Review](https://mlsanity.com/paper/2102.03889/) (2021)
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- [Supporting Safety Analysis of Image-processing DNNs through Clustering-based Approaches](https://mlsanity.com/paper/2301.13506/) (2023)
- [A Pathology-Based Machine Learning Method to Assist in Epithelial Dysplasia Diagnosis](https://mlsanity.com/paper/2204.03572/) (2022)
- [Data Efficient Contrastive Learning in Histopathology using Active Sampling](https://mlsanity.com/paper/2303.16247/) (2023)
- [Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images](https://mlsanity.com/paper/2412.03084/) (2024)
- [Leveraging Spatial Context for Positive Pair Sampling in Histopathology Image Representation Learning](https://mlsanity.com/paper/2503.05170/) (2025)
- [An ensemble deep learning approach to detect tumors on Mohs micrographic surgery slides](https://mlsanity.com/paper/2504.05219/) (2025)
- [Identifying regions of interest in whole slide images of renal cell carcinoma](https://mlsanity.com/paper/2504.07313/) (2025)
- [Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation](https://mlsanity.com/paper/2505.19746/) (2025)

## Abstract (arXiv)

There is a strong need for automated systems to improve diagnostic quality and reduce the analysis time in histopathology image processing. Automated detection and classification of pathological tissue characteristics with computer-aided diagnostic systems are a critical step in the early diagnosis and treatment of diseases. Once a pathology image is scanned by a microscope and loaded onto a computer, it can be used for automated detection and classification of diseases. In this study, the DenseNet-161 and ResNet-50 pre-trained CNN models have been used to classify digital histopathology patches into the corresponding whole slide images via transfer learning technique. The proposed pre-trained models were tested on grayscale and color histopathology images. The DenseNet-161 pre-trained model achieved a classification accuracy of 97.89% using grayscale images and the ResNet-50 model obtained the accuracy of 98.87% for color images. The proposed pre-trained models outperform state-of-the-art methods in all performance metrics to classify digital pathology patches into 24 categories.