Learning Riemannian metric for disease progression modeling
Authors: Samuel Gruffaz, Pierre-Emmanuel Poulet, Etienne Maheux, Bruno Jedynak, Stanley DURRLEMAN
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The metric update allows us to improve the forecasting of imaging and clinical biomarkers in the Alzheimer s Disease Neuroimaging Initiative (ADNI) cohort. Our results compare favorably to the 56 methods benchmarked in the TADPOLE challenge. |
| Researcher Affiliation | Academia | Samuel Gruffaz Inria Paris Ecole normale supérieure Paris-Saclay samuel.gruffaz@ens-paris-saclay.fr Pierre-Emmanuel Poulet Paris Brain Institute Inria Paris pierre-emmanuel.poulet@inria.fr Etienne Maheux Paris Brain Institute Inria Paris etienne.maheux@icm-institute.org Bruno Jedynak Maseeh Professor of Mathematical Sciences Fariborz Maseeh Hall, room 464P Portland, OR 97201 bruno.jedynak@pdx.edu Stanley Durrleman Paris Brain Institute Inria Paris Inserm, CNRS, Sorbonne University stanley.durrleman@inria.fr |
| Pseudocode | Yes | Algorithm 1 Geodesics Bending (Alternating maximization algorithm) |
| Open Source Code | No | All code will be available on Github in the near future. |
| Open Datasets | Yes | Data used in preparation of this article were obtained from the Alzheimer s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). ... The TADPOLE training set is composed of data from the first three ADNI phases (ADNI 1, ADNI GO and ADNI 2). |
| Dataset Splits | Yes | For both experiments, models are trained with a 5-folds cross-validation. |
| Hardware Specification | Yes | All the methods are developed in Python by extending the open-source Leaspy library (https://leaspy.readthedocs.io) created for DCM models and run on a 2.80GHz CPU with 16 GB RAM. |
| Software Dependencies | No | The paper mentions 'Python', 'Leaspy library', and 'scikit-learn library', but does not provide specific version numbers for any of these software components. |
| Experiment Setup | Yes | We choose σnoise = 0.01, ggen = gφ with φ(x) := exp(0.07(x + sin(x)) 1) and g = ., . for the data generation and Nc(σ = 0.08) 90 for the metric estimation... we choose k to be the Gaussian kernel... n MCMC = 200 (n MCMC = 10000 at the very first step...). |