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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Spatio-Angular Convolutions for Super-resolution in Diffusion MRI
Authors: Matthew Lyon, Paul Armitage, Mauricio A Álvarez
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the PCCNN performs competitively while using significantly fewer parameters. Moreover, we show that this formulation generalises well to clinically relevant downstream analyses such as fixel-based analysis, and neurite orientation dispersion and density imaging. ... 4 Experiments and Results |
| Researcher Affiliation | Academia | Matthew Lyon University of Manchester matthew.s.lyon@.manchester.ac.uk Paul Armitage University of Sheffield EMAIL Mauricio A Álvarez University of Manchester EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for this work is available at github.com/m-lyon/dmri-pcconv. |
| Open Datasets | Yes | d MRI data from the WU-Minn Human Connectome Project (HCP) [38] were used for training, validation and testing. |
| Dataset Splits | Yes | d MRI data from the WU-Minn Human Connectome Project (HCP) [38] were used for training, validation and testing. ... Models were trained on twenty-seven subjects from the HCP dataset, while three subjects were used for validation during development. |
| Hardware Specification | Yes | Models were trained using 4 NVIDIA A100 s with a batch size of 16, and an ℓ1 loss function, for 200,000 iterations using Adam W [23]. |
| Software Dependencies | No | The paper mentions software like 'Ray Tune [20]', 'MRtrix3 [37]', and 'cu DIMOT [17]' but does not provide specific version numbers for them. |
| Experiment Setup | Yes | Models were trained using 4 NVIDIA A100 s with a batch size of 16, and an ℓ1 loss function, for 200,000 iterations using Adam W [23]. ... Hyperparameters for the PCCNN were selected through a random grid search with Ray Tune [20]. ... Each PCConv layer or residual PCConv block is followed by a rectified linear unit (Re LU), excluding the final layer. ... Each hypernetwork is composed of two dense layers, each followed by a leaky Re LU with a negative slope of 0.1, and a final dense layer with output size 1 and no subsequent activation. ... The input angular dimension size was determined via qin U(qsample), qsample = {6, 7, ..., 19, 20}. |