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Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification

机译：基于互信息的任务驱动字典学习用于医学图像分类

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摘要

Objective: We present a novel variant of the bag-of-visual-words (BoVW) method for automated medical image classification. Methods: Our approach improves the BoVW model by learning a task-driven dictionary of the most relevant visual words per task using a mutual information-based criterion. Additionally, we generate relevance maps to visualize and localize the decision of the automatic classification algorithm. These maps demonstrate how the algorithm works and show the spatial layout of the most relevant words. Results: We applied our algorithm to three different tasks: chest x-ray pathology identification (of four pathologies: cardiomegaly, enlarged mediastinum, right consolidation, and left consolidation), liver lesion classification into four categories in computed tomography (CT) images and benign/malignant clusters of microcalcifications (MCs) classification in breast mammograms. Validation was conducted on three datasets: 443 chest x-rays, 118 portal phase CT images of liver lesions, and 260 mammography MCs. The proposed method improves the classical BoVW method for all tested applications. For chest x-ray, area under curve of 0.876 was obtained for enlarged mediastinum identification compared to 0.855 using classical BoVW (with p-value < 0.01). For MC classification, a significant improvement of 4% was achieved using our new approach (with p-value = 0.03). For liver lesion classification, an improvement of 6% in sensitivity and 2% in specificity were obtained (with p-value < 0.001). Conclusion: We demonstrated that classification based on informative selected set of words results in significant improvement. Significance: Our new BoVW approach shows promising results in clinically important domains. Additionally, it can discover relevant parts of images for the task at hand without explicit annotations for training data. This can provide computer-aided support for medical experts in challenging image analysis tasks.
机译：目的：我们提出了一种新颖的视觉词袋（BoVW）方法，用于医学图像自动分类。方法：我们的方法通过使用基于互信息的标准学习每个任务中最相关的视觉单词的任务驱动字典来改进BoVW模型。此外，我们生成相关性图以可视化和本地化自动分类算法的决策。这些地图演示了算法的工作原理，并显示了最相关的单词的空间布局。结果：我们将算法应用于以下三个不同的任务：胸部X射线病理学识别（四种病理学：心脏肥大，纵隔扩大，右巩固和左巩固），肝脏病变在计算机断层扫描（CT）图像中分为四类和良性/乳房X线照片中的微钙化（MCs）分类恶性簇。对三个数据集进行了验证：443例胸部X射线，118例肝脏病变的门期CT图像和260例乳房X线照片MC。所提出的方法改进了针对所有测试应用的经典BoVW方法。对于胸部X射线，与传统的BoVW（p值<0.01）的0.855相比，扩大纵隔可得到0.876的曲线下面积。对于MC分类，使用我们的新方法（p值= 0.03）实现了4％的显着改善。对于肝病灶分类，灵敏度提高了6％，特异性提高了2％（p值<0.001）。结论：我们证明了基于信息丰富的选定单词集进行分类可以显着改善。启示：我们的新BoVW方法在临床重要领域显示出可喜的结果。另外，它可以发现手头任务的图像相关部分，而无需显式注释训练数据。这可以为医学专家在具有挑战性的图像分析任务中提供计算机辅助支持。

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