Semantic Segmentation Models
UNet++
Introduced by Zhou et al. in UNet++: A Nested U-Net Architecture for Medical Image SegmentationUNet++ is an architecture for semantic segmentation based on the U-Net. Through the use of densely connected nested decoder sub-networks, it enhances extracted feature processing and was reported by its authors to outperform the U-Net in Electron Microscopy (EM), Cell, Nuclei, Brain Tumor, Liver and Lung Nodule medical image segmentation tasks.
Source: UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Semantic Segmentation | 21 | 19.27% |
| Image Segmentation | 14 | 12.84% |
| Medical Image Segmentation | 10 | 9.17% |
| Computed Tomography (CT) | 6 | 5.50% |
| Image Enhancement | 3 | 2.75% |
| Multi-Task Learning | 2 | 1.83% |
| Management | 2 | 1.83% |
| Clinical Knowledge | 2 | 1.83% |
| Change Detection | 2 | 1.83% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
