Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI. 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.
from plip.plip import PLIP
import numpy as np
plip = PLIP('vinid/plip')
# we create image embeddings and text embeddings
image_embeddings = plip.encode_images(images, batch_size=32)
text_embeddings = plip.encode_text(texts, batch_size=32)
# we normalize the embeddings to unit norm (so that we can use dot product instead of cosine similarity to do comparisons)
image_embeddings = image_embeddings/np.linalg.norm(image_embeddings, ord=2, axis=-1, keepdims=True)
text_embeddings = text_embeddings/np.linalg.norm(text_embeddings, ord=2, axis=-1, keepdims=True) from PIL import Image
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained("vinid/plip")
processor = CLIPProcessor.from_pretrained("vinid/plip")
image = Image.open("images/image1.jpg")
inputs = processor(text=["a photo of label 1", "a photo of label 2"],
images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1)
print(probs)
image.resize((224, 224))If you use PLIP in your research, please cite the following paper:
@article{huang2023visual,
title={A visual--language foundation model for pathology image analysis using medical Twitter},
author={Huang, Zhi and Bianchi, Federico and Yuksekgonul, Mert and Montine, Thomas J and Zou, James},
journal={Nature Medicine},
pages={1--10},
year={2023},
publisher={Nature Publishing Group US New York}
}The internal API has been copied from FashionCLIP.
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