Name
Abstract | ||
|---|---|---|
The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervised learning with well-paired image-level or voxel-level labels. However, labeling the lesion in medical images is laborious requiring highly specialized knowledge. We propose a medical image synthesis model named abnormal-to-normal translation generative adversarial network (ANT-GAN) to generate a normal-looking medical image based on its abnormal-looking counterpart without the need for paired training data. Unlike typical GANs, whose aim is to generate realistic samples with variations, our more restrictive model aims at producing a normal-looking image corresponding to one containing lesions, and thus requires a special design. Being able to provide a “normal” counterpart to a medical image can provide useful side information for medical imaging tasks like lesion segmentation or classification validated by our experiments. In the other aspect, the ANT-GAN model is also capable of producing highly realistic lesion-containing image corresponding to the healthy one, which shows the potential in data augmentation verified in our experiments. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JBHI.2020.2964016 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
Brain,Humans,Image Interpretation, Computer-Assisted,Unsupervised Machine Learning | Journal | 24 |
Issue | ISSN | Citations |
8 | 2168-2194 | 4 |
PageRank | References | Authors |
0.41 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liyan Sun | 1 | 11 | 2.22 |
| Jiexiang Wang | 2 | 9 | 1.48 |
| Yue Huang | 3 | 35 | 6.24 |
| Xinghao Ding | 4 | 591 | 52.95 |
| Hayit Greenspan | 5 | 2645 | 319.45 |
| John Paisley | 6 | 54 | 4.63 |