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..
Breaking Information Isolation: Accelerating MRI via Inter-sequence Mapping and Progressive Masking
Authors: Jianwei Zheng, Xiaomin Yao, Guojiang Shen, Wei Li, Jiawei Jiang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Massive experiments are conducted under various sampling patterns and acceleration rates, whose results demonstrate that, without any sophisticated architectures, our IMA outperforms the current cutting-edge methods both visually and numerically. Codes are available as an attachment and will be publicly released. |
| Researcher Affiliation | Academia | Jianwei Zheng, Xiaomin Yao, Guojiang Shen, Wei Li, and Jiawei Jiang* Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the model optimization and network design using mathematical equations and descriptive text, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Codes are available as an attachment and will be publicly released. |
| Open Datasets | Yes | We evaluate our proposal on two publicly available datasets: IXI (578 registered T2 and PD image pairs, 256 256) and knee fast MRI (240 PD image pairs, 320 320). |
| Dataset Splits | Yes | The data is split into training, validation, and testing sets (7:1:2). |
| Hardware Specification | Yes | All experiments are conducted using Py Torch on an NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify a version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | For our IMA, we use l1-norm loss, the Adam optimizer (0.9, 0.999), 100 epochs, an initial learning rate of 10 4, batch size of 1, ratio=0.9, γ = 0.5, δ = 3, and T=12. |