Breaking the Dilemma of Medical Image-to-image Translation
Authors: Lingke Kong, Chenyu Lian, Detian Huang, zhenjiang li, Yanle Hu, Qichao Zhou
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Performance evaluation of Reg GAN was conducted through three investigations to 1) demonstrate the feasibility and superiority of the Reg GAN mode in various methods, and 2) assess Reg GANs sensitivity to noise, and 3) explore the availability of the Reg GAN on unpaired data. The open-access dataset (Bra TS 2018[69]) was used to evaluate the proposed Reg GAN mode. The training dataset and testing dataset contained 8457 and 979 pairs of T1 and T2 MR images, respectively. Table 1 summarized the results for all methods and modes under the current investigation. |
| Researcher Affiliation | Collaboration | Lingke Kong Manteia Tech konglingke@manteiatech.com; Chenyu Lian Xiamen University cylian@stu.xmu.edu.cn; Detian Huang Huaqiao University huangdetian@hqu.edu.cn; Zhenjiang Li Shandong University zhenjli1987@163.com; Yanle Hu Mayo Clinic Arizona Hu.Yanle@mayo.edu; Qichao Zhou Manteia Tech zhouqc@manteiatech.com |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data used in this study can be found at https://github.com/Kid-Liet/Reg-GAN. |
| Open Datasets | Yes | The open-access dataset (Bra TS 2018[69]) was used to evaluate the proposed Reg GAN mode. The training dataset and testing dataset contained 8457 and 979 pairs of T1 and T2 MR images, respectively. |
| Dataset Splits | No | The paper mentions training and testing datasets with specific counts but does not provide specific information about a validation split, percentages for splits, or a cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU models, or memory. |
| Software Dependencies | No | The paper mentions using a "training strategy and hyperparameters for all methods and modes (see supplementary materials for details)" but does not specify software names with version numbers in the main text. |
| Experiment Setup | No | The paper states: "To ensure fair comparison, we used the same training strategy and hyperparameters for all methods and modes (see supplementary materials for details)." While hyperparameters are mentioned, the specific values or detailed settings are deferred to supplementary materials and not provided in the main text. |