PathologyFoundation / plipLinks
Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.
☆332Updated last year
Alternatives and similar repositories for plip
Users that are interested in plip are comparing it to the libraries listed below
Sorting:
- Vision-Language Pathology Foundation Model - Nature Medicine☆377Updated 3 months ago
- Multimodal Whole Slide Foundation Model for Pathology☆228Updated 3 months ago
- Pathology Foundation Model - Nature Medicine☆516Updated 3 months ago
- Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data☆514Updated 2 months ago
- ☆113Updated last year
- Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images - CVPR 2023☆104Updated 2 years ago
- [NeurIPS 2023 Oral] Quilt-1M: One Million Image-Text Pairs for Histopathology.☆162Updated last year
- Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction - CVPR 2024☆144Updated 7 months ago
- Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology - CVPR 2024☆136Updated 4 months ago
- Toolkit for large-scale whole-slide image processing.☆287Updated last week
- ☆319Updated 3 months ago
- Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images - ICCV 2021☆208Updated 3 years ago
- ☆184Updated last month
- DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image☆428Updated last year
- Standardized benchmark for computational pathology foundation models.☆87Updated 3 weeks ago
- CellViT: Vision Transformers for Precise Cell Segmentation and Classification☆306Updated 3 months ago
- Code associated to the publication: Scaling self-supervised learning for histopathology with masked image modeling, A. Filiot et al., Med…☆155Updated last year
- ☆112Updated last year
- Multimodal prototyping for cancer survival prediction - ICML 2024☆86Updated 4 months ago
- Ressources of histopathology datasets☆432Updated last week
- The official implementation of GPFM☆67Updated last month
- A curated list of foundation models for vision and language tasks in medical imaging☆268Updated last year
- Official Inplementation of 《WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole Slide Images》(MICCAI 2024 O…☆65Updated last month
- [CVPR 2024] Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology☆119Updated last month
- TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification☆410Updated last year
- ☆72Updated 6 months ago
- List of pathology feature extractors and foundation models☆138Updated last month
- TCGA Pathology Reports in Machine Readable Text☆48Updated last year
- ☆58Updated last month
- Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks - MICCAI 2021☆152Updated last year