Cross-cohort dementia identification using transfer learning with FDG-PET imaging
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Open Access
Type
Conference paperAbstract
Machine learning techniques have been extensively adapted to dementia identification with PET imaging in the last decade. Despite the promising results reported by these studies, the accurately labeled PET brain scans used to train machine learning models are generally difficult ...
See moreMachine learning techniques have been extensively adapted to dementia identification with PET imaging in the last decade. Despite the promising results reported by these studies, the accurately labeled PET brain scans used to train machine learning models are generally difficult to obtain in real clinic environments. To tackle this challenge, we proposed a dementia classification method inspired by transfer learning. The main idea is to train a machine learning model using an accurately labeled source image cohort and an unlabeled target image cohort jointly, and then use this model to label the unlabeled target cohort. We demonstrated the effectiveness of the knowledge transfer in dementia classification tasks by comparing the proposed method to several other methods on public and private image cohorts.
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See moreMachine learning techniques have been extensively adapted to dementia identification with PET imaging in the last decade. Despite the promising results reported by these studies, the accurately labeled PET brain scans used to train machine learning models are generally difficult to obtain in real clinic environments. To tackle this challenge, we proposed a dementia classification method inspired by transfer learning. The main idea is to train a machine learning model using an accurately labeled source image cohort and an unlabeled target image cohort jointly, and then use this model to label the unlabeled target cohort. We demonstrated the effectiveness of the knowledge transfer in dementia classification tasks by comparing the proposed method to several other methods on public and private image cohorts.
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Date
2018Source title
Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI 2018)Publisher
IEEEFunding information
ARC DP170104304Licence
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Faculty/School
Faculty of EngineeringShare