Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis
Authors: Bing Cao, Han Zhang, Nannan Wang, Xinbo Gao, Dinggang Shen10486-10493
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With experiments on multiple medical image databases, we demonstrate a great generalization ability as well as specialty of our method compared with other state-of-the-arts. |
| Researcher Affiliation | Academia | 1State Key Laboratory of Integrated Services Networks, Xidian University, Xi an, China 2Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA |
| Pseudocode | No | The paper describes the proposed framework and components in detail but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We conduct the first experiment on the Bra TS database (Menze et al. 2015), which consists of four MRI modalities: T1, T1-C, T2, and T2-FLAIR. For CT image translation, we evaluate our method on the Alzheimer s Disease Neuroimaging Initiative (ADNI) database, which has two modalities: T1 and CT. |
| Dataset Splits | Yes | We randomly select 80% subjects as a training set; the remaining 20% subjects are taken as a testing set. Such a process is repeated by 10 times. |
| Hardware Specification | Yes | We conduct all the experiments under the environment of Python 3.7 and Py Torch 1.0 on a Ubuntu 18.04 system with NVIDIA TITAN Xp GPU. |
| Software Dependencies | Yes | We conduct all the experiments under the environment of Python 3.7 and Py Torch 1.0 on a Ubuntu 18.04 system with NVIDIA TITAN Xp GPU. |
| Experiment Setup | Yes | In the training phase, the input images are resized to 256 256 and then cropped to the size of 240 240. In the testing phase, the input images are not resized. The trade-off parameter λ1 is set to 0.5 and λ2 is set to 10 in our experiments. |