Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease

By Jun Shi, Xiao Zheng, Yan Li, Shihui Ying. IEEE journal of biomedical and health informatics, 2018

379 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Neurology

View Paper: https://www.semanticscholar.org/paper/4f61779a850eff1fe544613a1769f02f0b145441

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mufti Mahmud
2. Amir Hussain
3. M. S. Kaiser
4. Stefano Vassanelli
5. Olivier Colliot
6. Mingxia Liu
7. Chunfeng Lian
8. Jun Zhang

In Neurology

1. Chunfeng Lian
2. Mingxia Liu
3. Dinggang Shen
4. Jun Zhang
5. Yuan Xue
6. Li-Jia Li
7. Chong Wang
8. Zhe Li

Citations in the corpus

• Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, methodology): “, 2015)​) and supervised structures (deep polynomial networks ​(Shi et al., 2018)​), and an SVM is used for classification.”
• Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis using Structural MRI (2018, methodology): “Our proposed H-FCN method is also different from existing deep-learning-based AD diagnosis methods in the literature [31], [33], [34], [50], [51], [66], [68].”
• Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, methodology): “[44], [46], [48], [64], [104], [123] modeled the fMRI data as spatial maps, and then used them as the input of the classification models.”
• DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images (2021, positive): “Shi et al. [20] proposed deep polynomial network which performs well for both small and large dataset to diagnose AD.”
• Deep Learning in Neuroradiology (2018, background): “Another study 34 showed that a multimodal stacked deep polynomial network using the ADNI dataset could classify patients into different binary groups (ie, AD versus healthy control [NC], or mild cognitive impairment converters [MCI-C] versus nonconverters [MCI-nonconverters]).”
• Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “Several models were applied to detect Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) from MRI and PET scans including DA [117,118], DBM [120], RBM [121], and multimodal stacked deep polynomial network (MM-SDPN) [124].”
• Thoracic Disease Identification and Localization with Limited Supervision (2017, background): “It is usually formulated as a classification problem where medical imaging abnormalities are identified as different clinical conditions [25, 4, 26, 28, 34].”