# Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology

arXiv: https://arxiv.org/abs/2204.08311 · HTML: https://mlsanity.com/paper/2204.08311/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 114 references that are in the map.
- First posted on arXiv 18 Apr 2022.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Going deeper with Image Transformers](https://mlsanity.com/paper/2103.17239/) (2021)
- [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference](https://mlsanity.com/paper/2104.01136/) (2021)
- [Pay Attention to MLPs](https://mlsanity.com/paper/2105.08050/) (2021)
- [Bottleneck Transformers for Visual Recognition](https://mlsanity.com/paper/2101.11605/) (2021)
- [ResMLP: Feedforward networks for image classification with data-efficient training](https://mlsanity.com/paper/2105.03404/) (2021)
- [Co-Scale Conv-Attentional Image Transformers](https://mlsanity.com/paper/2104.06399/) (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)
- [A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks](https://mlsanity.com/paper/2003.12255/) (2020)
- [Gastric histopathology image segmentation using a hierarchical conditional random field](https://mlsanity.com/paper/2003.01302/) (2020)
- [A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis](https://mlsanity.com/paper/2009.13721/) (2020)
- [MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification](https://mlsanity.com/paper/2108.10709/) (2021)
- [Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks](https://mlsanity.com/paper/1909.11870/) (2019)
- [A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification](https://mlsanity.com/paper/2102.10499/) (2021)
- [Look, Investigate, and Classify: A Deep Hybrid Attention Method for Breast Cancer Classification](https://mlsanity.com/paper/1902.10946/) (2019)
- [Highly Efficient Follicular Segmentation in Thyroid Cytopathological Whole Slide Image](https://mlsanity.com/paper/1902.05431/) (2019)
- [Histopathologic Image Processing: A Review](https://mlsanity.com/paper/1904.07900/) (2019)

## Built on by (most cited first, 6 of 6)

- [Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection](https://mlsanity.com/paper/2311.09942/) (2023)
- [Histopathologic Cancer Detection](https://mlsanity.com/paper/2311.07711/) (2023)
- [Federated and Transfer Learning for Cancer Detection Based on Image Analysis](https://mlsanity.com/paper/2405.20126/) (2024)
- [pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems](https://mlsanity.com/paper/2308.06983/) (2023)
- [Enhancing Early Alzheimer Disease Detection through Big Data and Ensemble Few-Shot Learning](https://mlsanity.com/paper/2510.19282/) (2025)
- [An approach with Visual and Tabular Mamba to multimodal medical data using Mixed Fusion](https://mlsanity.com/paper/2606.20738/) (2026)

## Abstract (arXiv)

Background: Breast cancer has the highest prevalence in women globally. The classification and diagnosis of breast cancer and its histopathological images have always been a hot spot of clinical concern. In Computer-Aided Diagnosis (CAD), traditional classification models mostly use a single network to extract features, which has significant limitations. On the other hand, many networks are trained and optimized on patient-level datasets, ignoring the application of lower-level data labels. Method: This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions of breast histopathological images. First, the BreaKHis dataset is randomly divided into a training, validation and test set. Then, data augmentation techniques are used to balance the number of benign and malignant samples. Thirdly, considering the performance of transfer learning and the complementarity between each network, VGG16, Xception, ResNet50, DenseNet201 are selected as the base classifiers. Result: In the ensemble network model with accuracy as the weight, the image-level binary classification achieves an accuracy of 98.90%. In order to verify the capabilities of our method, the latest Transformer and Multilayer Perception (MLP) models have been experimentally compared on the same dataset. Our model wins with a 5%-20% advantage, emphasizing the ensemble model's far-reaching significance in classification tasks. Conclusion: This research focuses on improving the model's classification performance with an ensemble algorithm. Transfer learning plays an essential role in small datasets, improving training speed and accuracy. Our model has outperformed many existing approaches in accuracy, providing a method for the field of auxiliary medical diagnosis.