# Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks

arXiv: https://arxiv.org/abs/1710.05726 · HTML: https://mlsanity.com/paper/1710.05726/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2017.

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

- [Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddings](https://mlsanity.com/paper/2102.07611/) (2021)
- [Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets](https://mlsanity.com/paper/2109.05025/) (2021)
- [Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images](https://mlsanity.com/paper/2110.14013/) (2021)
- [An evaluation of pre-trained models for feature extraction in image classification](https://mlsanity.com/paper/2310.02037/) (2023)
- [Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning](https://mlsanity.com/paper/2109.06547/) (2021)
- [Immunohistochemistry Biomarkers-Guided Image Search for Histopathology](https://mlsanity.com/paper/2304.12424/) (2023)
- [Classification of Microscopy Images of Breast Tissue: Region Duplication based Self-Supervision vs. Off-the Shelf Deep Representations](https://mlsanity.com/paper/2202.06073/) (2022)
- [Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer](https://mlsanity.com/paper/2212.14652/) (2022)
- [Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study](https://mlsanity.com/paper/2309.17223/) (2023)
- [Batch Augmentation with Unimodal Fine-tuning for Multimodal Fusion of Large Language Models](https://mlsanity.com/paper/2505.06592/) (2025)

## Abstract (arXiv)

We explore the problem of classification within a medical image data-set based on a feature vector extracted from the deepest layer of pre-trained Convolution Neural Networks. We have used feature vectors from several pre-trained structures, including networks with/without transfer learning to evaluate the performance of pre-trained deep features versus CNNs which have been trained by that specific dataset as well as the impact of transfer learning with a small number of samples. All experiments are done on Kimia Path24 dataset which consists of 27,055 histopathology training patches in 24 tissue texture classes along with 1,325 test patches for evaluation. The result shows that pre-trained networks are quite competitive against training from scratch. As well, fine-tuning does not seem to add any tangible improvement for VGG16 to justify additional training while we observed considerable improvement in retrieval and classification accuracy when we fine-tuned the Inception structure.