Self-weighted Multi-task Learning for Subjective Cognitive Decline Diagnosis
| Autor: | Jiahui Su, Yichen Wei, Gaoxiong Duan, Nina Cheng, Denao Deng, Zhiguo Zhang, Xiucheng Nong, Lihua Zhao, Baiying Lei, Bihan Yu, Wei Mai, Chong Li, Tianfu Wang, Alejandro F. Frangi |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Multi-task learning Feature selection 02 engineering and technology Machine learning computer.software_genre Task (project management) 03 medical and health sciences Identification (information) 0302 clinical medicine Neuroimaging Discriminative model 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Cognitive decline business Cognitive impairment computer 030217 neurology & neurosurgery |
| Zdroj: | Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597276 MICCAI (7) |
| Popis: | Subjective cognitive decline (SCD) is an early stage of mild cognitive impairment (MCI) and may represent the first symptom manifestation of Alzheimer’s disease (AD). Early diagnosis of MCI is important because early identification and intervention can delay or even reverse the progression of this disease. This paper proposes an automatic diagnostic framework for SCD and MCI. Specifically, we design a new multi-task learning model to integrate neuroimaging functional and structural connectivity in a predictive framework. We construct a functional brain network by sparse low-rank brain network estimation methods, and a structural brain network is constructed using fiber bundle tracking. Subsequently, we use multi-task learning methods to select features for integrated functional and structural connections, the importance of each task and the balance between both modalities are automatically learned. By integrating both functional and structural information, the most discriminative features of the disease are obtained for diagnosis. The experiments on the dataset show that our proposed method achieves good performance and is superior to the traditional algorithms. In addition, the proposed method can identify the most discriminative brain regions and connections. These results follow current clinical findings and add new findings for disease detection and future medical analysis. |
| Databáze: | OpenAIRE |
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