This model is designed for tumor classification in medical imaging. It should be used as part of a larger medical diagnostic system and not as a standalone diagnostic tool.

Usage

python

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image

# Load model and processor
processor = AutoImageProcessor.from_pretrained("SIATCN/vit_tumor_classifier")
model = AutoModelForImageClassification.from_pretrained("SIATCN/vit_tumor_classifier")

# Load and process image
image = Image.open("path_to_your_image.jpg")
inputs = processor(image, return_tensors="pt")

# Make prediction
outputs = model(**inputs)
predictions = outputs.logits.softmax(dim=-1)
predicted_label = predictions.argmax().item()
confidence = predictions[0][predicted_label].item()

# Get class name
class_names = ["non-tumor", "tumor"]
print(f"Predicted: {class_names[predicted_label]} (confidence: {confidence:.2f})")