Medmnist_active_learning

MedMNIST Active Learning Model

Overview

This model is designed for image classification tasks within the medical imaging domain, specifically targeting the MedMNIST dataset. It employs a ResNet-50 architecture tailored for 28x28 pixel images and incorporates active learning strategies to enhance performance with limited labeled data.

Model Architecture

• Base Model: ResNet-50
• Modifications:
  • Adjusted initial convolution layer to accommodate 28x28 input images.
  • Removed max pooling layer to preserve spatial dimensions.
  • Customized fully connected layer to output predictions for 9 classes.

Training Procedure

Training Hyperparameters

Optimizer Settings

The optimizer used during training was Stochastic Gradient Descent(SDG), with the following settings and a Learning Rate Scheduler of ReduceLROnPlateau:

• learning_rate = 0.01332344940133225
• momentum = 0.9
• weight_decay = 0.00021921795989143406

The model was trained with float32 precision.

Dataset

PathMNIST

Data Augmentation

• Random resized cropping
• Horizontal flipping
• Random rotations
• Color jittering
• Gaussian blur
• RandAugment

Active Learning Strategy

The active learning process was based on a mixed sampling strategy:

• Uncertainty Sampling: Monte Carlo (MC) dropout was used to estimate uncertainty.
• Diversity Sampling: K-means clustering was employed to ensure diverse samples.

Evaluation

The model was evaluated on the validation set of PathMNIST. Key performance metrics include:

• Accuracy: 94.72%
• Loss: 0.2397
• AUC: 99.73%

Graphs

The following plots illustrates the validation loss, validation accuracy, and validation auc over batches(number of iterations over the dataset) during the active learning process.

• Validation Loss
• Validation Accuracy
• Validation AUC

Usage

All code for this model can be accessed in the following GitHub Repository: Allen Cheung Determined_AI_Hackathon

To utilize this model:

1. Install Dependencies: Ensure the following Python packages are installed:

   • torch
   • torchvision
   • medmnist
   • scikit-learn
   • determined

   Install them using pip:

   pip install torch torchvision medmnist scikit-learn determined
   

2. Load the Model:

   import torch
   from model import ResNet50_28
   
   model = ResNet50_28(num_classes=9)
   model.load_state_dict(torch.load('pytorch_model.bin'))
   model.eval()
   

3. Inference:

   from torchvision import transforms
   from PIL import Image
   
   transform = transforms.Compose([
       transforms.Resize((28, 28)),
       transforms.ToTensor(),
       transforms.Normalize(mean=[0.5], std=[0.5])
   ])
   
   image = Image.open('path_to_image.jpg')
   input_tensor = transform(image).unsqueeze(0)
   output = model(input_tensor)
   prediction = output.argmax(dim=1).item()
   print(f"Predicted class: {prediction}")
   

License

This project is licensed under the MIT License.

Acknowledgements

• MedMNIST Dataset
• Determined AI
• Survey on Deep Active Learning: Wang, H., Jin, Q., Li, S., Liu, S., Wang, M., & Song, Z. (2024). A comprehensive survey on deep active learning in medical image analysis. Medical Image Analysis, 95, 103201. https://doi.org/10.1016/j.media.2024.103201