医学图像语义概念识别方法研究

doi:10.3969/j.issn.0258-8021.2019.03.007

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Abstract Identifying useful concepts from large scale medical images is an important technology for image knowledge representation. Developing semantic concept detection algorithms is helpful to machine understanding and learning latent knowledge from medical images, and plays an important role in image-assisted diagnosis and intelligent image reading. In this study, the problem of detecting high-frequency concepts from medical images was transformed into a multi-label classification task. The deep transfer learning method based on convolutional neural network (CNNs) was used to recognize high-frequency medical concepts. The image retrieval-based topic modeling method was used to obtain the semantically related concepts from the similar images of given medical images. The CLEF cross language image retrieval track (ImageCLEF) launched the ImageCLEFcaption 2018 evaluation task on May, 2018, in which the Concept Detection subtask identified 111,156 semantic concepts from 222,314 training images and 9,938 test images. Our research group presented experimental results of both methods. The CNNs-based deep transfer learning method achieved the F1 score of 0.0928, which ranked second in all the submission teams. The retrieval-based topic model could recall some low-frequency concepts and achieved the F1 score of 0.0907, but dependent heavily on the image retrieval results. The results proved that the CNNs-based deep transfer learning method showed preferable robustness on high-frequency concept detection, but still a lot of room for improvement in the research of large-scale open semantic concept detection.

Key words： concept detection deep transfer learning multi-label classification medical image retrieval topic model

Received: 22 November 2018

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2019.03.007 OR http://cjbme.csbme.org/EN/Y2019/V38/I3/306

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