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Explainable breast cancer prediction from 3-dimensional dynamic contrast-enhanced magnetic resonance imaging
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Suya Han ,
Naveed Urr Rehman ,
Kanwal Ahmed,
Hassan Eshkiki ,
Fabio Caraffini
Applied Intelligence, Volume: 55, Issue: 13
Swansea University Authors:
Hassan Eshkiki , Fabio Caraffini
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DOI (Published version): 10.1007/s10489-025-06803-9
Abstract
Deep learning models have been instrumental in extracting critical indicators for breast cancer diagnosis - the prevalent malignancy among women worldwide - from baseline magnetic resonance imaging. However, many existing models do not fully leverage the rich spatial information available in the 3D...
| Published in: | Applied Intelligence |
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| ISSN: | 0924-669X 1573-7497 |
| Published: |
Springer Science and Business Media LLC
2025
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70043 |
| Abstract: |
Deep learning models have been instrumental in extracting critical indicators for breast cancer diagnosis - the prevalent malignancy among women worldwide - from baseline magnetic resonance imaging. However, many existing models do not fully leverage the rich spatial information available in the 3D structure of medical imaging data, potentially overlooking important contextual details. This develops an explainable deep learning framework for classifying breast cancer that leverages the complete 3D and provides classification results alongside visual explanations of the decision-making process. The preprocessing pipeline is fed with 3D sequences containing ‘tumour’ and ‘non-tumour’ regions. It includes a 3D Adaptive Unsharp Mask (AUM) filter to reduce noise and augment image class, followed by normalisation and data augmentation. Classification is then achieved by training an augmented ResNet150 model. Three explainable artificial intelligence (XAI) techniques, including Shapley Additive Explanations, 3D Gradient-Weighted Class Activation Mapping, and Contextual Importance and Utility, are employed to provide improved interpretability. The model demonstrates state-of-the-art performance over the QIN-BREAST dataset, achieving testing accuracies of 98.861% for ‘tumours’ and 99.447% for ‘non-tumours’, as well as over the Duke Breast Cancer Dataset, where it achieves 99.104% for ‘tumours’ and 99.753% for ‘non-tumours’, while offering enhanced interpretability through XAI methods. |
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| Keywords: |
Breast cancer; Deep learning; DCE-MRI; Explainable AI; RESNET150 |
| College: |
Faculty of Science and Engineering |
| Funders: |
Swansea University |
| Issue: |
13 |