Distant Domain Transfer Learning for Medical Imaging

Medical image processing is one of the most important topics in the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical imaging tasks. In this paper, we propose a novel transfer learning framework for medical image classificati...

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Bibliographic Details
Main Authors:	Liu, M. (Author), Liu, Y. (Author), Niu, S. (Author), Song, H. (Author), Wang, J. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	Article Biological organs Chest X-ray image Classification (of information) Classification accuracy Classification models computer assisted tomography Computerized tomography convolutional neural network coronavirus disease 2019 COVID-19 COVID-19 diagnosis COVID-19 Testing deep learning Deep learning Diagnosis diagnostic imaging Diagnostic Imaging distant domain transfer learning distant feature fusion Distant transfer distant transfer learning domain adaptation feature extraction human Humans image analysis image processing image segmentation internet of medical things internet of things kernel hilbert space kernel method Labeled training data sets learning algorithm Learning frameworks Learning systems machine learning Machine Learning medical image processing Medical imaging nonhuman performance Privacy by design SARS-CoV-2 Segmentation models selective learning algorithm State-of-the-art performance thorax radiography transfer learning Transfer learning transfer of learning
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Distant Domain Transfer Learning for Medical Imaging

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