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首页> 外文会议>International Conference on Image and Vision Computing New Zealand >Modality Classification and Concept Detection in Medical Images Using Deep Transfer Learning
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Modality Classification and Concept Detection in Medical Images Using Deep Transfer Learning

机译：使用深度转移学习的医学图像模式分类和概念检测

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摘要

Medical image classification and concept detection are two important tasks for efficient and robust medical retrieval systems and also help with downstream tasks such as knowledge discovery, medical report generation, medical question answering, and clinical decision making. We investigate the effectiveness of transfer learning on the modality classification task using state-of-the-art deep convolutional neural networks pretrained on generic images. We also compare the performance of the traditional pipeline of handcrafted features with multi-label learning algorithms with end-to-end deep learning features for the concept detection task. Experimental results on the modality classification task show that transfer learning can leverage the patterns learned from large training data to the medical domain where little labeled data is available. Moreover, results on the concept detection task show that the deep learning approach provides better and more powerful feature representations compared to handcrafted feature extraction methods. The results on both tasks suggest that deep transfer learning methods are effective in the medical domain where data is scarce.
机译：医学图像分类和概念检测是有效而强大的医学检索系统的两个重要任务，并且还有助于完成下游任务，例如知识发现，医学报告生成，医学问题解答和临床决策。我们使用在通用图像上预训练的最先进的深度卷积神经网络，研究在模态分类任务上进行转移学习的有效性。我们还将传统手工功能的流水线与多标签学习算法以及端到端深度学习功能用于概念检测任务的性能进行了比较。模态分类任务的实验结果表明，转移学习可以利用从大型训练数据到几乎没有标记数据的医学领域学习的模式。此外，概念检测任务的结果表明，与手工特征提取方法相比，深度学习方法提供了更好，更强大的特征表示。两项任务的结果都表明，深度转移学习方法在数据稀缺的医学领域是有效的。

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