Dynamic model scaling based on segmented tumor size for breast cancer detection
<p>The accuracy of breast cancer detection in histopathology images presents a critical challenge and remains a central focus in advancements in computational pathology. Scaling Convolutional Neural Networks (CNNs) can improve feature extraction, especially in multi-class problems that require...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2025
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| الملخص: | <p>The accuracy of breast cancer detection in histopathology images presents a critical challenge and remains a central focus in advancements in computational pathology. Scaling Convolutional Neural Networks (CNNs) can improve feature extraction, especially in multi-class problems that require varying levels of complexity to distinguish between different classes. However, selecting the optimal model complexity for each image remains a persistent challenge. Current approaches predominantly rely on fixed-complexity models regardless of individual image characteristics. This study introduces an adaptive method that intelligently matches model complexity to tumor characteristics by integrating a Denoising Diffusion Probabilistic Model (DDPM), a generative model that iteratively improves segmentation accuracy by progressively removing noise, for tumor region segmentation with a range of EfficientNet architectures for classification. The segmentation model, trained on a specialized dataset of breast cancer regions, identifies tumor regions in testing images from breast cancer detection datasets. Based on the size of the segmented tumor region, an appropriate EfficientNet model, ranging from B0 to B7, is dynamically selected for classification. The core insight is that smaller or undetected tumor regions require less computational complexity and can be accurately classified using efficient models like EfficientNet-B0, while larger tumor regions benefit from the enhanced feature extraction capabilities of deeper models like EfficientNet-B7 to capture more intricate features. This adaptive approach mitigates segmentation inaccuracies and ensures that the appropriate model complexity is applied based on tumor characteristics. Rigorously evaluated on three distinct datasets, the proposed approach demonstrates superior performance compared to leading techniques, particularly in terms of 95% confidence intervals, the area under the precision–recall curve (PR-AUC), and accuracy, achieving 93.86% accuracy with 95% confidence intervals of [93.15–95.31%] and PR-AUC of 0.986 on the BRACS dataset. This approach represents a notable advancement in breast cancer detection within histopathology images and holds significant potential as a tool in computational pathology.</p><h2>Other Information</h2> <p> Published in: Biomedical Signal Processing and Control<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.bspc.2025.109118" target="_blank">https://dx.doi.org/10.1016/j.bspc.2025.109118</a></p> |
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