Modelpublic

StanfordAIMI/CheXficient

sourceHugging Facemitupdated 7mo agoView on Hugging Face

3likes48kdownloads

Model Card

CheXficient

CheXficient is a vision-language foundation model for chest X-ray (CXR) interpretation, designed to improve both data efficiency and computational efficiency during pretraining.

Instead of scaling indiscriminately to ever-larger datasets, CheXficient adopts a principled data curation strategy to selectively prioritize informative training samples. This approach demonstrates that active, structured data selection can serve as a cost-effective alternative to brute-force dataset enlargement.

The model follows a dual-encoder architecture and supports prompt-based zero-shot classification via joint image-text representation learning.

Model Overview

—Architecture: Vision-language dual encoder
—Image Backbone: DINOv2 (base)
—Text Backbone: BioClinicalBERT
—Input: Chest X-ray image + text prompts
—Output: Image-text similarity logits and embeddings
—Framework: PyTorch + Hugging Face Transformers
—Intended Use: Research in medical AI and multimodal learning

Installation

bash

pip install torch torchvision transformers pillow

Load the Model

python

import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer, AutoImageProcessor

repo_id = "StanfordAIMI/CheXficient"
device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModel.from_pretrained(
    repo_id,
    trust_remote_code=True
).to(device)

tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
image_processor = AutoImageProcessor.from_pretrained(repo_id, trust_remote_code=True)

model.eval()

Zero-Shot Classification Example

python

image = Image.open("./CXR/images/5AF3BB6C1BCC83C.png").convert("RGB")
text = ["Pneumonia", "no Pneumonia"]

image_inputs = image_processor(images=image, return_tensors="pt").to(device)
text_inputs = tokenizer(text, padding=True, return_tensors="pt").to(device)

with torch.no_grad():
    outputs = model(
        pixel_values=image_inputs["pixel_values"],
        text_tokens=text_inputs,
    )

print(outputs)

Optional probability conversion:

python

import torch.nn.functional as F

logits = outputs["logits_per_image"]
probs = F.softmax(logits, dim=-1)
print(probs)

Citation

bibtex

@article{wang2026data,
  title={A data-and compute-efficient chest X-ray foundation model beyond aggressive scaling},
  author={Wang, Chong and Zhang, Yabin and Gao, Yunhe and Varma, Maya and Mottez, Clemence and Patsatzi, Faidra and Liu, Jiaming and Long, Jin and Delbrouck, Jean-Benoit and Gatidis, Sergios and others},
  journal={arXiv preprint arXiv:2602.22843},
  year={2026}
}