MedCLIP

Wang, Zifeng and Wu, Zhenbang and Agarwal, Dinesh and Sun, Jimeng. (2022). MedCLIP: Contrastive Learning from Unpaired Medical Images and Texts. EMNLP'22.

Paper PDF

Download MedCLIP

Before download MedCLIP, you need to find feasible torch version (with GPU) on https://pytorch.org/get-started/locally/.

Then, download MedCLIP by

pip install git+https://github.com/RyanWangZf/MedCLIP.git

# or

pip install medclip

Three lines to get pretrained MedCLIP models

from medclip import MedCLIPModel, MedCLIPVisionModelViT, MedCLIPVisionModel

# load MedCLIP-ResNet50
model = MedCLIPModel(vision_cls=MedCLIPVisionModel)
model.from_pretrained()

# load MedCLIP-ViT
model = MedCLIPModel(vision_cls=MedCLIPVisionModelViT)
model.from_pretrained()

As simple as using CLIP

from medclip import MedCLIPModel, MedCLIPVisionModelViT
from medclip import MedCLIPProcessor
from PIL import Image

# prepare for the demo image and texts
processor = MedCLIPProcessor()
image = Image.open('./example_data/view1_frontal.jpg')
inputs = processor(
    text=["lungs remain severely hyperinflated with upper lobe emphysema", 
        "opacity left costophrenic angle is new since prior exam ___ represent some loculated fluid cavitation unlikely"], 
    images=image, 
    return_tensors="pt", 
    padding=True
    )

# pass to MedCLIP model
model = MedCLIPModel(vision_cls=MedCLIPVisionModelViT)
model.from_pretrained()
model.cuda()
outputs = model(**inputs)
print(outputs.keys())
# dict_keys(['img_embeds', 'text_embeds', 'logits', 'loss_value', 'logits_per_text'])

MedCLIP for Prompt-based Classification

from medclip import MedCLIPModel, MedCLIPVisionModelViT
from medclip import MedCLIPProcessor
from medclip import PromptClassifier

processor = MedCLIPProcessor()
model = MedCLIPModel(vision_cls=MedCLIPVisionModelViT)
model.from_pretrained()
clf = PromptClassifier(model, ensemble=True)
clf.cuda()

# prepare input image
from PIL import Image
image = Image.open('./example_data/view1_frontal.jpg')
inputs = processor(images=image, return_tensors="pt")

# prepare input prompt texts
from medclip.prompts import generate_chexpert_class_prompts, process_class_prompts
cls_prompts = process_class_prompts(generate_chexpert_class_prompts(n=10))
inputs['prompt_inputs'] = cls_prompts

# make classification
output = clf(**inputs)
print(output)
# {'logits': tensor([[0.5154, 0.4119, 0.2831, 0.2441, 0.4588]], device='cuda:0',
#       grad_fn=<StackBackward0>), 'class_names': ['Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Pleural Effusion']}