Methods › Computer Vision › Vision and Language Pre-Trained Models › PLIP
Pathology Language and Image Pre-Training
PLIP
Introduced by Zhi Huang et al. in Leveraging medical Twitter to build a visual–language foundation model for pathology AI
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Pathology Language and Image Pre-Training (PLIP) is a vision-and-language foundation model created by fine-tuning CLIP on pathology images.
Papers archive 2025-07-28
5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification 11 Feb 2025 · 1 repository · arXiv:2502.07409
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Leveraging Computational Pathology AI for Noninvasive Optical Imaging Analysis Without Retraining 18 Nov 2024 · 0 repositories · arXiv:2411.11613
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Benchmarking PathCLIP for Pathology Image Analysis 5 Jan 2024 · 0 repositories · arXiv:2401.02651
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PLIP: Language-Image Pre-training for Person Representation Learning 15 May 2023 · 1 repository · arXiv:2305.08386Syntology ran 0 of 9 samples · 9 unverified
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Leveraging medical Twitter to build a visual–language foundation model for pathology AI 1 Apr 2023 · 1 repository
Tasks archive 2025-07-28
20 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections