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首页> 外文期刊>IEEE Transactions on Medical Imaging >Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches
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Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches

机译：深度学习时代的肺和胰腺肿瘤表征：新型的有监督和无监督学习方法

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Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and foster personalized treatment planning as a part of precision medicine. In this papet, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains with deep learning algorithms, particularly by utilizing a 3D convolutional neural network and transfer learning. Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task-dependent feature representations into a CAD system via a graph-regularized sparse multi-task learning framework. In the second approach, we explore an unsupervised learning algorithm to address the limited availability of labeled training data, a common problem in medical imaging applications. Inspired by learning from label proportion approaches in computer vision, we propose to use proportion-support vector machine for characterizing tumors. We also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification. We evaluate our proposed supervised and unsupervised learning algorithms on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans, respectively, and obtain the state-of-the-art sensitivity and specificity results in both problems.
机译：使用计算机辅助诊断（CAD）工具可以更准确，更快地根据放射图像对肿瘤进行风险分层（特征化）。通过此类工具进行的肿瘤表征还可以实现非侵入性癌症分期，预后，并促进个性化治疗计划作为精密医学的一部分。在本专题中，我们提出了有监督和无监督的机器学习策略，以改善肿瘤特征。我们的第一种方法是基于监督学习，为此我们证明了其在深度学习算法方面的显著成果，尤其是通过利用3D卷积神经网络和转移学习。由放射学家对扫描的解释所激发，然后我们展示了如何通过图规范化的稀疏多任务学习框架将与任务相关的特征表示整合到CAD系统中。在第二种方法中，我们探索了一种无监督的学习算法，以解决标记的训练数据的有限可用性，这是医学成像应用中的常见问题。受计算机视觉中标签比例方法学习的启发，我们建议使用比例支持向量机来表征肿瘤。我们还寻求有关“深层特征”对无监督肿瘤分类的好处这一基本问题的答案。我们针对两种不同的肿瘤诊断挑战（分别通过1018 CT和171 MRI扫描对肺和胰腺进行评估）评估了我们提出的有监督和无监督学习算法，并在这两个问题中获得了最新的敏感性和特异性结果。

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