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A Multi-Label Classification with an Adversarial-Based Denoising Autoencoder for Medical Image Annotation

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机构： [1]School of Management of Hefei University of Technology, Key Laboratory of Process Optimization and Intelligence Decision Making, Minister of Education, Hefei, China [2]Research Center for Contemporary Management, School of Economics and Management, Tsinghua University, Beijing, China [3]Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China [4]Kelley School of Business, Indiana University, Indiana, USA

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关键词： Deep learning adversarial learning medical image annotation multi-label classification denoising autoencoder

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Medical image annotation aims to automatically describe the content of medical images. It helps doctors to understand the content of medical images and make better informed decisions like diagnoses. Existing methods mainly follow the approach for natural images and fail to emphasize the object abnormalities, which is the essence of medical images annotation. In light of this, we propose to transform the medical image annotation to a multi-label classification problem, where object abnormalities are focused directly. However, extant multi-label classification studies rely on arduous feature engineering, or do not solve label correlation issues well in medical images. To solve these problems, we propose a novel deep learning model where a frequent pattern mining component and an adversarial-based denoising autoencoder component are introduced. Extensive experiments are conducted on a real retinal image dataset to evaluate the performance of the proposed model. Results indicate that the proposed model significantly outperforms image captioning baselines and multi-label classification baselines.

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出版当年[2021]版：

无

最新[2023]版：

Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

影响因子： 2.5 最新[2023版] 3.2 最新五年平均 0 出版当年[2021版] 0 出版当年五年平均 0 出版前一年[2020版] 2.5 出版后一年[2022版]

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第一作者机构： [1]School of Management of Hefei University of Technology, Key Laboratory of Process Optimization and Intelligence Decision Making, Minister of Education, Hefei, China

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