# Explainable Ensemble Machine Learning for Breast Cancer Diagnosis based on Ultrasound Image Texture Features

arXiv: https://arxiv.org/abs/2201.07227 · HTML: https://mlsanity.com/paper/2201.07227/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 17 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [Stakeholders in Explainable AI](https://mlsanity.com/paper/1810.00184/) (2018)
- [Deep Learning Based Computer-Aided Systems for Breast Cancer Imaging : A Critical Review](https://mlsanity.com/paper/2010.00961/) (2020)
- [A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine Learning Approach](https://mlsanity.com/paper/2002.01441/) (2020)

## Built on by (most cited first, 5 of 5)

- [Breast Cancer Image Classification Method Based on Deep Transfer Learning](https://mlsanity.com/paper/2404.09226/) (2024)
- [Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI](https://mlsanity.com/paper/2404.03892/) (2024)
- [OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound Video Synthesis](https://mlsanity.com/paper/2308.08269/) (2023)
- [Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework for Breast Cancer Detection and Segmentation](https://mlsanity.com/paper/2308.14213/) (2023)
- [Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2507.07148/) (2025)

## Abstract (arXiv)

Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in performance, are black-box systems that provide minimal insight into the inner logic behind their predictions. This is a major drawback as the explainability of prediction is vital for applications such as cancer diagnosis. In this paper, we address this issue by proposing an explainable machine learning pipeline for breast cancer diagnosis based on ultrasound images. We extract first- and second-order texture features of the ultrasound images and use them to build a probabilistic ensemble of decision tree classifiers. Each decision tree learns to classify the input ultrasound image by learning a set of robust decision thresholds for texture features of the image. The decision path of the model predictions can then be interpreted by decomposing the learned decision trees. Our results show that our proposed framework achieves high predictive performance while being explainable.