论文标题
半监督乳房质量分类的虚拟对抗训练
Virtual Adversarial Training for Semi-supervised Breast Mass Classification
论文作者
论文摘要
这项研究旨在开发一种新型的计算机辅助诊断(CAD)方案,以使用半监督学习的学习来开发乳房X线乳房乳房质量分类。尽管受到监督的深度学习在各种医学图像分析任务中取得了巨大的成功,但其成功依赖于大量高质量注释,这在实践中可能具有挑战性。为了克服这一局限性,我们建议采用一种半监督的方法,即虚拟对抗训练(VAT),以利用和学习未标记数据中的有用信息,以更好地分类乳房肿块。因此,我们的基于增值税的模型有两种类型的损失,即受监督和虚拟对抗性损失。前者的损失在监督分类中起作用,而后一种损失旨在增强对虚拟对抗扰动的模型鲁棒性,从而改善了模型的推广性。为了评估我们基于增值税的CAD方案的性能,我们回顾性地组装了1024个乳腺质量图像,良性和恶性肿瘤数量相等。在这项研究中使用了一个大的CNN和一个小的CNN,并且都接受了对抗性损失的训练。当标记的比率为40%和80%时,基于增值税的CNN分别提供了最高分类精度为0.740和0.760。实验结果表明,基于增值税的CAD方案可以有效利用从未标记数据的有意义的知识来更好地对乳房X线乳房乳房质量图像进行分类。
This study aims to develop a novel computer-aided diagnosis (CAD) scheme for mammographic breast mass classification using semi-supervised learning. Although supervised deep learning has achieved huge success across various medical image analysis tasks, its success relies on large amounts of high-quality annotations, which can be challenging to acquire in practice. To overcome this limitation, we propose employing a semi-supervised method, i.e., virtual adversarial training (VAT), to leverage and learn useful information underlying in unlabeled data for better classification of breast masses. Accordingly, our VAT-based models have two types of losses, namely supervised and virtual adversarial losses. The former loss acts as in supervised classification, while the latter loss aims at enhancing model robustness against virtual adversarial perturbation, thus improving model generalizability. To evaluate the performance of our VAT-based CAD scheme, we retrospectively assembled a total of 1024 breast mass images, with equal number of benign and malignant masses. A large CNN and a small CNN were used in this investigation, and both were trained with and without the adversarial loss. When the labeled ratios were 40% and 80%, VAT-based CNNs delivered the highest classification accuracy of 0.740 and 0.760, respectively. The experimental results suggest that the VAT-based CAD scheme can effectively utilize meaningful knowledge from unlabeled data to better classify mammographic breast mass images.