MicroMIL: Graph-based Contextual Multiple Instance Learning for Patient Diagnosis Using Microscopic Images

Overview

Requirements

pip install -r requirements.txt 

How to Run

conda create -n micromil python=3.9
source activate py39
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia

Execute MicroMIL on dataset

python main.py

Arguments

• --lr: Learning rate

  • Description: Specifies the learning rate used by the optimizer during training.

• --weight_decay: Weight decay

  • Description: Controls the amount of L2 regularization applied to the model's parameters to prevent overfitting.

• --batch_size: Batch size

  • Description: Determines the number of samples per batch used for training.

• --cluster_number: Cluster number

  • Description: Specifies the number of clusters or groups used in the model.

• --epoch: Number of epochs

  • Description: Sets the total number of complete passes through the dataset during training.

• --patience: Patience

  • Description: Number of epochs with no improvement after which training will be stopped if using early stopping.

• --model_name: Model name

  • Description: Name of the model architecture or type to be used (e.g., 'resnet18', 'regnet_y_400mf', etc.).

• --seed: Random seed

  • Description: Seed value used to initialize random number generators for reproducibility.

• --layer: Number of layers

  • Description: Number of layers in the neural network architecture.

• --device: Device

  • Description: Device (e.g., 'cuda:0', 'cpu') on which to run the model.

• --shuffle: Shuffle

  • Description: whether to shuffle the data during training.

• --hidden_dim: Hidden dimension

  • Description: Dimensionality of the hidden layers in the neural network.

• --num_classes: Number of classes

  • Description: Number of output classes or categories for the classification task.

• --dropout_node: Dropout rate for nodes

  • Description: Dropout rate applied to node features in the graph neural network.

• --type: Model type

  • Description: Type of the model architecture or framework used (e.g., 'graph', 'CNN', etc.).