Discriminative Localized Sparse Approximations for Mass Characterization in Mammograms.

The most common form of cancer among women in both developed and developing countries is breast cancer. The early detection and diagnosis of this disease is significant because it may reduce the number of deaths caused by breast cancer and improve the quality of life of those effected. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) methods have shown promise in recent years for aiding in the human expert reading analysis and improving the accuracy and reproducibility of pathology results. One significant application of CADe and CADx is for breast cancer screening using mammograms. In image processing and machine learning research, relevant results have been produced by sparse analysis methods to represent and recognize imaging patterns. However, application of sparse analysis techniques to the biomedical field is challenging, as the objects of interest may be obscured because of contrast limitations or background tissues, and their appearance may change because of anatomical variability. We introduce methods for label-specific and label-consistent dictionary learning to improve the separation of benign breast masses from malignant breast masses in mammograms. We integrated these approaches into our Spatially Localized Ensemble Sparse Analysis (SLESA) methodology. We performed 10- and 30-fold cross validation (CV) experiments on multiple mammography datasets to measure the classification performance of our methodology and compared it to deep learning models and conventional sparse representation. Results from these experiments show the potential of this methodology for separation of malignant from benign masses as a part of a breast cancer screening workflow.

在发达国家和发展中国家，女性中最常见的癌症形式是乳腺癌。这种疾病的早期检测和诊断意义重大，因为它可能减少乳腺癌导致的死亡人数，并提高受影响者的生活质量。近年来，计算机辅助检测（CADe）和计算机辅助诊断（CADx）方法在辅助人类专家解读分析以及提高病理结果的准确性和可重复性方面显示出了良好的前景。CADe和CADx的一个重要应用是利用乳房X光检查进行乳腺癌筛查。在图像处理和机器学习研究中，稀疏分析方法在表示和识别成像模式方面已经产生了相关成果。然而，将稀疏分析技术应用于生物医学领域具有挑战性，因为感兴趣的对象可能由于对比度限制或背景组织而被遮挡，并且它们的外观可能由于解剖结构的变异性而改变。我们引入了针对特定标签和标签一致的字典学习方法，以提高乳房X光片中乳腺良性肿块和恶性肿块的区分能力。我们将这些方法整合到我们的空间局部集成稀疏分析（SLESA）方法中。我们在多个乳房X光检查数据集上进行了10折和30折交叉验证（CV）实验，以衡量我们方法的分类性能，并将其与深度学习模型和传统的稀疏表示进行比较。这些实验的结果显示了这种方法作为乳腺癌筛查工作流程的一部分，在区分恶性肿块和良性肿块方面的潜力。