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..
DDM$^2$: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
Authors: Tiange Xiang, Mahmut Yurt, Ali B Syed, Kawin Setsompop, Akshay Chaudhari
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on 4 real-world in-vivo diffusion MRI datasets and show that our DDM2 demonstrates superior denoising performances ascertained with clinically-relevant visual qualitative and quantitative metrics. |
| Researcher Affiliation | Academia | Tiange Xiang, Mahmut Yurt, Ali B Syed, Kawin Setsompop & Akshay Chaudhari Stanford University EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | Our source codes are available at: https://github.com/Stanford MIMI/DDM2. |
| Open Datasets | Yes | To evaluate the generalizability of DDM2, additional experiments were done on 3 other publicly-available brain diffusion MRI datasets acquired with different protocols with less advanced MRI encoding for image SNR and resolution: (i) Sherbrooke 3-Shell dataset (Garyfallidis et al., 2014); (ii) Stanford HARDI (Rokem, 2016); (iii) Parkinson s Progression Markers Initiative (PPMI) dataset (Marek et al., 2011). |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | Yes | All experiments were performed on RTX Ge Force 2080-Ti GPUs in Py Torch (Paszke et al., 2019). |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify a version number for it or any other software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | The Adam optimizer was used to optimize both networks with a fixed learning rate of 1e-4 and a batch size of 32. We trained Φ for 1e4 steps and F for 1e5 steps from scratch. |