Healthcare & Medical Imaging🧬🩻
Collection
This collection focuses on medical imaging under data scarcity, combining diffusion-based synthetic data generation with transformer-based model. • 3 items • Updated
Unconditional Image Generation
Diffusers
Safetensors
English
DDPMPipeline
Lung
Pneumonia
Covid-19
PyTorch
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("teohyc/Covid-XRay-Diffusion-Model", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Diffusion Model for COVID-19 X-ray Generation
This is a diffusion model designed for generating synthetic COVID-19 X-ray images. The model takes random noise as input and iteratively denoises it to produce realistic X-ray images. Used to generate synthetic xray image for scarce COVID-19 positive cases, which can be used for data augmentation in training diagnostic models.
Training data from https://data.mendeley.com/datasets/9xkhgts2s6/4 Full project file at https://github.com/teohyc/covid_xray_diffusion
##Usage
from diffusers import DDPMPipeline
import matplotlib.pyplot as plt
# Load the pipeline
pipeline = DDPMPipeline.from_pretrained("teohyc/Covid-XRay-Diffusion-Model")
# Generate a synthetic X-ray
image = pipeline(num_inference_steps=500).images[0] #default is 1000 steps, but you can reduce it for faster generation (at the cost of quality)
# Display
plt.imshow(image, cmap='gray')
plt.axis('off')
plt.show()
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