Vision and Language Pre-Trained Models
Pathology Language and Image Pre-Training
Introduced by Huang et al. in Leveraging medical Twitter to build a visual–language foundation model for pathology AIPathology Language and Image Pre-Training (PLIP) is a vision-and-language foundation model created by fine-tuning CLIP on pathology images.
Source: Leveraging medical Twitter to build a visual–language foundation model for pathology AI
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Benchmarking | 1 | 11.11% |
| Decision Making | 1 | 11.11% |
| Image Classification | 1 | 11.11% |
| Image Retrieval | 1 | 11.11% |
| Retrieval | 1 | 11.11% |
| Zero-Shot Learning | 1 | 11.11% |
| Pedestrian Attribute Recognition | 1 | 11.11% |
| Person Re-Identification | 1 | 11.11% |
| Text based Person Retrieval | 1 | 11.11% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
