Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns
Authors: Zhijian Yang, Junhao Wen, Christos Davatzikos
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We first validated the model through semi-synthetic experiments, and then demonstrated its potential in capturing biologically plausible imaging patterns in Alzheimer s disease (AD). |
| Researcher Affiliation | Academia | Zhijian Yang1,2, Junhao Wen1 and Christos Davatzikos1 1Center for Biomedical Image Computing and Analytics, University of Pennsylvania 2Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania |
| Pseudocode | Yes | Detailed training procedure of Surreal-GAN is disclosed by Algorithm 1. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. There is no mention of a repository link, explicit code release statement, or code in supplementary materials. |
| Open Datasets | Yes | For AD, we defined the CN group (N=850) to be subjects with Mini-mental state examination (MMSE) scores above 29, and the PT group (N=2204) as subjects diagnosed as mild cognitive impairment (MCI) or AD at baseline. |
| Dataset Splits | Yes | A five fold cross validation were run with three different models. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like "ADAM optimizer" and "AFNI-3dttest" but does not provide specific version numbers for these or other ancillary software dependencies required for replication. |
| Experiment Setup | Yes | ADAM optimizer(Kingma & Ba (2014)) was used with a learning rate (lr) 4 10 5 for Discriminator and 2 10 4 for transformation function f and clustering function g. β1 and β2 are set to be 0.5 and 0.999 respectively. For hyper-parameters, we set γ = 6, κ = 80, ζ = 80, µ = 500, η = 6... the batch size was set to be 1/8 of the PT data sample sizes. The model was trained for at least 100000 iterations... |