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..
Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and Reconstruction
Authors: Pengcheng Lei, Faming Fang, Guixu Zhang, Ming Xu
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our MC-CDic model on multicontrast MRI SR and reconstruction tasks. Experimental results demonstrate the superior performance of the proposed MC-CDic model against existing SOTA methods. Code is available at https://github.com/lpcccc-cv/MC-CDic. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, East China Normal University 2Software Engineering Institute, East China Normal University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/lpcccc-cv/MC-CDic. |
| Open Datasets | Yes | Following [Lyu et al., 2020; Fang et al., 2022], we employ two publicly available multi-modal MR image datasets, IXI and Bra TS2018 [Menze et al., 2015] to validate the effectiveness of our model. |
| Dataset Splits | Yes | We splitted the two datasets patient-wisely into a ratio of 7:1:2 for training/validation/testing. |
| Hardware Specification | Yes | Our proposed MC-CDic model is implemented in PyTorch with an NVIDIA RTX3090 GPU. |
| Software Dependencies | No | The paper mentions "PyTorch" but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | We adopt the Adam optimizer with a batch size of 6. The learning rate is set to 1e-4 and the models are trained 50 epochs. |