vit-xray-v1

ViT X-ray Multi-label (vit-xray-v1)

Model Description

This model is a fine-tuned Vision Transformer (google/vit-base-patch16-224-in21k) for multi-label classification of chest X-rays.
It predicts the presence of multiple findings such as:

• Nodule
• Infiltration
• Effusion
• Atelectasis

Author: Om Kumar (Hugging Face: @itsomk)

The model is designed for research and educational purposes only and should not be used as a substitute for clinical diagnosis.

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Intended Use

• Research in medical imaging and computer vision
• Educational purposes for understanding X-ray image classification
• Baseline model for further fine-tuning or domain adaptation

⚠️ Not intended for clinical use. Predictions should not guide medical decisions.

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Training Data

• Dataset: Chest X-ray images (publicly available datasets, e.g., NIH ChestX-ray14, etc.)
• Images were preprocessed (resized to 224x224, normalized).
• Labels are multi-label, meaning an X-ray can contain more than one finding.

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Model Performance

• Optimized for detecting common thoracic abnormalities.
• Evaluation metrics: AUC .
• Nodule AUC: 0.696
• Infiltration AUC: 0.684
• Effusion AUC: 0.843
• Atelectasis AUC: 0.762

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Quick Usage

from transformers import AutoImageProcessor, AutoModelForImageClassification
import torch
from PIL import Image

MODEL = "itsomk/vit-xray-v1"
processor = AutoImageProcessor.from_pretrained(MODEL)
model = AutoModelForImageClassification.from_pretrained(MODEL)

img = Image.open("path/to/xray.jpg").convert("RGB")
inputs = processor(images=img, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

probs = torch.sigmoid(logits).squeeze().tolist()
results = {model.config.id2label[i]: float(probs[i]) for i in range(len(probs))}
print(results)