增强乳腺癌诊断:基于DenseNet的迁移学习与神经散列的组织病理学细粒度图像分类。

IF 2.6 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Fatemeh Taheri, Kambiz Rahbar
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引用次数: 0

摘要

乳腺癌是世界上最常见的癌症之一。乳腺癌病例的数量突出了各级疾病管理的重要性。显微成像是乳腺癌分类的一种补充方法。人工组织病理学图像分析耗时且容易出现人为错误。由于广泛的进步,计算机辅助诊断(CAD)已经成为一种流行和可行的医学图像分析解决方案。显微图像分析可以帮助医生进行更准确的诊断。然而,实际应用中,CAD模型的性能还有待改进。在提出的方法中,考虑了一个称为DenseNet的基线模型,用于从组织病理学图像中提取特征。预训练的DenseNet模型本身不足以用于良性和恶性组织病理图像样本之间的细粒度特征区分。因此,在网络的末端加入了两个哈希层,以增强良性和恶性两类特征的可分离性。在BreakHis组织病理学图像数据集上对该方法的性能进行了评估,放大倍数分别为40倍、100倍、200倍和400倍。评价结果证实了该方法与其他现有方法的有效性。此外,使用LIME技术证明了所提出方法的可解释性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing breast cancer diagnosis: transfer learning on DenseNet with neural hashing for histopathology fine-grained image classification.

Breast cancer is one of the most common types of cancer worldwide. The number of breast cancer cases highlights the importance of disease management at various levels. One complementary method for breast cancer classification is microscopic imaging. Manual histopathological image analysis is time-consuming and prone to human errors. Computer-aided diagnosis (CAD) has emerged as a popular and feasible solution for analyzing medical images due to extensive advancements. Microscopic image analysis can assist physicians in more accurate diagnosis. However, the performance of CAD models needs improvement for practical purposes. In the proposed approach, a baseline model called DenseNet is considered for extracting features from histopathological images. The pre-trained DenseNet model alone is not sufficient for fine-grained feature discrimination between benign and malignant histopathological image samples. Therefore, two hash layers are incorporated at the end of the network to enhance feature separability of the two classes, benign and malignant. The performance of the proposed method is evaluated on the BreakHis histopathological image dataset, with magnifications of 40 × , 100 × , 200 × , and 400 × . The evaluation results confirm the effectiveness of the proposed approach compared to other existing approaches. Furthermore, the interpretability of the proposed approach is demonstrated using the LIME technique.

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来源期刊
Medical & Biological Engineering & Computing
Medical & Biological Engineering & Computing 医学-工程:生物医学
CiteScore
6.00
自引率
3.10%
发文量
249
审稿时长
3.5 months
期刊介绍: Founded in 1963, Medical & Biological Engineering & Computing (MBEC) continues to serve the biomedical engineering community, covering the entire spectrum of biomedical and clinical engineering. The journal presents exciting and vital experimental and theoretical developments in biomedical science and technology, and reports on advances in computer-based methodologies in these multidisciplinary subjects. The journal also incorporates new and evolving technologies including cellular engineering and molecular imaging. MBEC publishes original research articles as well as reviews and technical notes. Its Rapid Communications category focuses on material of immediate value to the readership, while the Controversies section provides a forum to exchange views on selected issues, stimulating a vigorous and informed debate in this exciting and high profile field. MBEC is an official journal of the International Federation of Medical and Biological Engineering (IFMBE).
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