Abstract
In this paper, we propose a novel method for MRI-based AD/MCI diagnosis that systematically integrates voxel-based, region-based, and patch-based approaches in a unified framework. Specifically, we parcellate a brain into predefined regions by using anatomical knowledge, i.e., template, and find complex nonlinear relations among voxels, whose intensity denotes the volumetric measure in our case, within each region. Unlike the existing methods that mostly use a cubical or rectangular shape, we regard the anatomical shape of regions as atypical forms of patches. Using the complex nonlinear relations among voxels in each region learned by deep neural networks, we extract a regional abnormality representation. We then make a final clinical decision by integrating the regional abnormality representations over a whole brain. It is noteworthy that the regional abnormality representations allow us to interpret and understand the symptomatic observations of a subject with AD or MCI by mapping and visualizing them in a brain space individually. We validated the efficacy of our method in experiments with baseline MRI dataset in the ADNI cohort by achieving promising performances in three binary classification tasks.
Original language | English |
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Title of host publication | Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings |
Editors | Mingxia Liu, Heung-Il Suk, Yinghuan Shi |
Publisher | Springer Verlag |
Pages | 64-72 |
Number of pages | 9 |
ISBN (Print) | 9783030009182 |
DOIs | |
Publication status | Published - 2018 |
Event | 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 2018 Sept 16 → 2018 Sept 16 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11046 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 |
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Country/Territory | Spain |
City | Granada |
Period | 18/9/16 → 18/9/16 |
Bibliographical note
Funding Information:Acknowledgement. This work was partially supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A01052216); and also by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP (2016941946).
Funding Information:
This work was partially supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A01052216); and also by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP (2016941946).
Publisher Copyright:
© Springer Nature Switzerland AG 2018.
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science