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首页> 外文会议>International conference on intelligent computing >Integration of Multimodal Data for Breast Cancer Classification Using a Hybrid Deep Learning Method
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Integration of Multimodal Data for Breast Cancer Classification Using a Hybrid Deep Learning Method

机译：使用混合深度学习方法整合用于乳腺癌分类的多峰数据

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Although the application of deep learning has greatly improved the performance of benign and malignant breast cancer classification algorithm, the accuracy of classification using only the pathological image has been unable to meet the requirements of clinical practice. Inspired by the real scene when the pathologist read the pathological image for diagnosis, in this paper, we propose a new hybrid deep learning method for benign and malignant breast cancer classification. From the perspective of multimodal data fusion, our method combines pathological image and structured data in the clinical electronic medical record (EMR) to further improve the accuracy of breast cancer classification. Thus, the proposed method can be useful for breast cancer diagnosis in real clinical practice. Experimental results based on our datasets show that the proposed method significantly outperforms the state-of-the-art methods in terms of overall classification accuracy.
机译：尽管深度学习的应用极大地改善了良性和恶性乳腺癌分类算法的性能，但是仅使用病理图像进行分类的准确性仍无法满足临床实践的要求。受到病理学家阅读病理图像进行诊断的真实场景的启发，本文提出了一种新的混合深度学习方法，用于对良性和恶性乳腺癌进行分类。从多模式数据融合的角度来看，我们的方法将病理图像和临床电子病历（EMR）中的结构化数据相结合，以进一步提高乳腺癌分类的准确性。因此，所提出的方法可用于实际临床实践中的乳腺癌诊断。基于我们的数据集的实验结果表明，在整体分类准确性方面，该方法明显优于最新方法。

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