Breast cancer is now considered as one of the leading causes of deaths among women all over the world. Aiming to assist clinicians in improving the accuracy of diagnostic decisions, computer-aided diagnosis (CAD) system is of increasing interest in breast cancer detection and analysis nowadays. In this paper, a novel computer-aided diagnosis scheme with human-in-the-loop is proposed to help clinicians identify the benign and malignant breast tumors in ultrasound. In this framework, feature acquisition is performed by a user-participated feature scoring scheme that is based on Breast Imaging Reporting and Data System (BI-RADS) lexicon and experience of doctors. Biclustering mining is then used as a useful tool to discover the column consistency patterns on the training data. The patterns frequently appearing in the tumors with the same label can be regarded as a potential diagnostic rule. Subsequently, the diagnostic rules are utilized to construct component classifiers of the Adaboost algorithm via a novel rules combination strategy which resolves the problem of classification in different feature spaces (PC-DFS). Finally, the AdaBoost learning is performed to discover effective combinations and integrate them into a strong classifier. The proposed approach has been validated using a large ultrasounic dataset of 1,062 breast tumor instances (including 418 benign cases and 644 malignant cases) and its performance was compared with several conventional approaches. The experimental results show that the proposed method yielded the best prediction performance, indicating a good potential in clinical applications.

中文翻译：

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结合双聚类挖掘和 AdaBoost 进行乳腺肿瘤分类

乳腺癌现在被认为是全世界女性死亡的主要原因之一。为了帮助临床医生提高诊断决策的准确性，计算机辅助诊断 (CAD) 系统在当今的乳腺癌检测和分析中越来越受到关注。在本文中，提出了一种具有人在回路的新型计算机辅助诊断方案，以帮助临床医生通过超声识别乳腺肿瘤的良恶性。在这个框架中，特征获取是通过用户参与的特征评分方案来执行的，该方案基于乳房成像报告和数据系统 (BI-RADS) 词典和医生的经验。然后使用双聚类挖掘作为发现训练数据列一致性模式的有用工具。在具有相同标签的肿瘤中频繁出现的模式可以被视为潜在的诊断规则。随后，利用诊断规则通过一种新的规则组合策略来构建 Adaboost 算法的组件分类器，该策略解决了不同特征空间中的分类问题（PC-DFS）。最后，执行 AdaBoost 学习以发现有效组合并将它们集成到一个强分类器中。所提出的方法已使用包含 1,062 个乳腺肿瘤实例（包括 418 个良性病例和 644 个恶性病例）的大型超声数据集进行了验证，并将其性能与几种传统方法进行了比较。实验结果表明，所提出的方法产生了最好的预测性能，表明在临床应用中具有良好的潜力。