Semi-supervised Learning Approaches for Predicting Lung Nodules Semantic Characteristics
نویسندگان
چکیده
We propose two semi-supervised learning approaches for automatically predicting semantic characteristics of lung nodules based on low-level image features. The NIH Lung Image Database Consortium (LIDC) dataset is used for training and testing of the proposed approaches such that the nodules on which at least three radiologists agree serve as labeled data and all the other nodules serve as unlabeled data. We show that, in the case of the LIDC [1] dataset, we are able to improve the accuracy prediction by 50% in average when using our proposed semisupervised approaches versus the traditional supervised classification approaches. While this paper briefly explains our methodology and results, the extended version of this paper has been accepted as a journal publication [2] that will appear this year.
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