Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion
Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion
复制标题
DOI：
10.1016/j.compbiomed.2020.103954
复制
发表时间：
2020-09-01
影响因子：
7.7
通讯作者：
Joseph, Paul K.
中科院分区：
文献类型：
--
作者：
George, Kalpana;Faziludeen, Shameer;Joseph, Paul K.
背景与目的：乳腺癌是女性常见的恶性肿瘤，其死亡率极高。发展中国家的死亡率相对较高，原因是缺乏早期检测设施，以及在获得防治这一疾病的技术进步方面受到限制。确诊癌症的唯一方法是由病理学家进行活检。计算机辅助诊断算法可以帮助病理学家在诊断过程中更加高效，客观和一致。这项工作的重点是开发一个可靠的自动化乳腺癌诊断方法，可以在当前的临床environment.Methods：核重叠和复杂的结构组织的活检图像中的乳腺组织核分割，特征提取和分类的挑战。在这项工作中，提出了一种核引导迁移学习（NucTraL）方法，作为一种简单且负担得起的乳腺肿瘤分类算法。图像特征由局部细胞核特征的融合表示，这些特征是使用在ImageNet数据库上预训练的卷积神经网络（CNN）模型提取的。在这项工作中使用的细胞核补丁提取策略避免了精细分割的细胞核边界，但提供了良好的区分能力的分类功能。使用支持向量机（SVM）分类器将融合特征分类为良性和恶性类别。然后，采用基于信念理论的分类器融合（BCF）策略来联合收割机从不同的CNN-SVM组合产生的输出，以进一步提高准确性。结果：通过执行100个随机试验，70%-30%的训练，以测试公共可用的BreakHis数据集上的划分，实现了结果的评估。该框架的平均准确率为96.91%，敏感性为97.24%，特异性为96.18%。结论：NucTraL+BCF框架在不需要高计算能力的情况下，仍能获得与现有方法相当的结果。这种基于迁移学习的定性框架可以为开发具有成本效益和低复杂度的组织病理学图像乳腺癌诊断CAD系统做出重要贡献。
Background and objective : Breast cancer is a frequently diagnosed cancer in women, contributing to significant mortality rates. Death rates are relatively higher in developing nations due to the shortage of early detection amenities and constraints on access to technical advances combating this disease. The only way to diagnose cancer with certainty is through biopsy performed by pathologists. Computer-aided diagnostic algorithms can assist pathologists in being more productive, objective and consistent in the diagnostic process. The focus of this work is to develop a reliable automated breast cancer diagnosis method which can operate in the prevailing clinical environment.Methods: Nuclei overlap and complex structural organisation of the breast tissue in biopsy images make nuclei segmentation, feature extraction and classification challenging. In this work, a nucleus guided transfer learning (NucTraL) methodology is proposed as a simple and affordable breast tumor classification algorithm. The image feature is represented by fusion of local nuclei features that are extracted using convolutional neural network (CNN) models pretrained on the ImageNet database. The nucleus patch extraction strategy used in this work avoids fine segmentation of the nuclei boundary but provides features with good discriminative power for classification. Classification of the fused features into benign and malignant classes is performed using a support vector machine (SVM) classifier. A belief theory based classifier fusion (BCF) strategy is then employed to combine the outputs arising from the different CNN-SVM combinations to improve accuracy further.Results: Evaluation of results is achieved by executing 100 random trials with 70%-30% train to test division on the publicly available BreaKHis dataset. The proposed framework achieved average accuracy of 96.91%, sensitivity of 97.24% and specificity of 96.18%.Conclusion: It is found that the proposed NucTraL+BCF framework outperforms several recent approaches and achieves results comparable to the state-of-the-art methods even without using high computational power. This qualitative framework based on transfer learning can contribute significantly for developing cost effective and low complexity CAD system for breast cancer diagnosis from histopathological images.