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Deep Multiple Instance Learning with Distance-Aware Self-Attention

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.vscode
.vscode
 
 
conf
conf
 
 
mil
mil
 
 
sweeps
sweeps
 
 
.gitignore
.gitignore
 
 
Dockerfile
Dockerfile
 
 
README.md
README.md
 
 
collect_trial_stats.ipynb
collect_trial_stats.ipynb
 
 
dataset.ipynb
dataset.ipynb
 
 
docker-compose.yml
docker-compose.yml
 
 
install_cpu.sh
install_cpu.sh
 
 
install_gpu.sh
install_gpu.sh
 
 
make_summary.py
make_summary.py
 
 
mnist_visualisation.ipynb
mnist_visualisation.ipynb
 
 
print_config.py
print_config.py
 
 
requirements.txt
requirements.txt
 
 
run.py
run.py
 
 
run.sh
run.sh
 
 
run_agent_for_sweeps_cpu.sh
run_agent_for_sweeps_cpu.sh
 
 
run_experiment.sh
run_experiment.sh
 
 
run_sweep_cpu.sh
run_sweep_cpu.sh
 
 
run_trials_for_selected_model_gpu.sh
run_trials_for_selected_model_gpu.sh
 
 
run_trials_for_selected_models_cpu.sh
run_trials_for_selected_models_cpu.sh
 
 
start_agents_gpu.sh
start_agents_gpu.sh
 
 
start_remote_agents_cpu.sh
start_remote_agents_cpu.sh
 
 
start_sweeps_cpu.py
start_sweeps_cpu.py
 
 
test.py
test.py
 
 
to_test.sh
to_test.sh
 
 
train.ipynb
train.ipynb
 
 
train.py
train.py
 
 

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Deep Multiple Instance Learning with Distance-Aware Self-Attention (DAS-MIL)

Paper link

Installation

If you're using a GPU, run:

./install_gpu.sh

Otherwise, use:

./install_cpu.sh

Training

MNIST-COLLAGE

python3 train.py +selected_model/mnist_collage=distance_aware_self_attention +experiment=mnist_collage

MNIST-COLLAGE-INV

python3 train.py +selected_model/mnist_collage=distance_aware_self_attention +experiment=mnist_collage_inverse

CAMELYON16

python3 train.py +selected_model/camelyon16=distance_aware_self_attention

You can use the option device=0 to use GPU 0. Full list of options is available at conf/config.yaml. We use hydra for configuration management.

Ablations

See the YAML files in conf/selected_model/mnist_collage_ablations. Run ./run_trials_for_selected_models_cpu.sh to run all the ablations (set selected_model_type="mnist_collage_ablations" in the shell script).

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