Multi-Layer Multi-View Classification for Alzheimer’s Disease Diagnosis
Authors: Changqing Zhang, Ehsan Adeli, Tao Zhou, Xiaobo Chen, Dinggang Shen
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on Alzheimers Disease Neuroimaging Initiative (ADNI) data sets validate the effectiveness of our proposed method. |
| Researcher Affiliation | Academia | a Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, USA b School of Computer Science and Technology, Tianjin University, Tianjin, China c Department of Psychiatry and Behavioral Sciences & Stanford AI Lab (SAIL), Stanford University, California, USA |
| Pseudocode | Yes | Algorithm 1: Optimization for our ML-MVC model. |
| Open Source Code | No | No statement about making the source code publicly available for the described methodology was found. |
| Open Datasets | Yes | Experimental results on Alzheimers Disease Neuroimaging Initiative (ADNI) data sets validate the effectiveness of our proposed method. ... we download ADNI 1.5T MR and PET images from the ADNI website 2. ... 2http://adni.loni.usc.edu/ |
| Dataset Splits | Yes | In all experiments, the data are split into 10 non-overlapping folds with 9/10 and 1/10 as training and testing data, and reporting the average results and standard deviation. We conduct standard 10-fold crossvalidation for each split with the hyperparameters selected from {0.01, 0.1, 1, 10, 100} for α, and {0.1, 1, 10, 100} for the other hyperparameters. |
| Hardware Specification | No | No specific details about the hardware (e.g., CPU/GPU models, memory, or specific computing environments) used for running the experiments were provided. |
| Software Dependencies | No | We employ a support vector classification model as the basic classifier which is from the LIBSVM toolbox 1 publicly available for the compared methods. |
| Experiment Setup | Yes | We conduct standard 10-fold crossvalidation for each split with the hyperparameters selected from {0.01, 0.1, 1, 10, 100} for α, and {0.1, 1, 10, 100} for the other hyperparameters. Gaussian kernel is employed for each type of features, i.e., k(xi, xj) = φ(xi), φ(xj) = exp( ||xi xj||2 / 2σ2 ) where σ = median({||xi xj||}i =j). Initialize: P(1) = = P(V ) = 0, G = K = W = 0, ρ = 1.2, ϵ = 10 6, maxμ =106; Initialize S with random values. |