Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Learning Riemannian metric for disease progression modeling
Authors: Samuel Gruffaz, Pierre-Emmanuel Poulet, Etienne Maheux, Bruno Jedynak, Stanley DURRLEMAN
NeurIPS 2021 | Venue PDF | 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 EMAIL Pierre-Emmanuel Poulet Paris Brain Institute Inria Paris EMAIL Etienne Maheux Paris Brain Institute Inria Paris EMAIL Bruno Jedynak Maseeh Professor of Mathematical Sciences Fariborz Maseeh Hall, room 464P Portland, OR 97201 EMAIL Stanley Durrleman Paris Brain Institute Inria Paris Inserm, CNRS, Sorbonne University EMAIL |
| 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...). |