heejong-kim / lilac

Learning-based Inference of Longitudinal imAge Changes (LILAC)

Learning-based Inference of Longitudinal imAge Changes (LILAC)

Overview

LILAC is a model that learns to compare images, designed to automatically filter out nuisance variations and focus on clinically significant changes in longitudinal imaging data.

This repository includes models and train/evaluation code for the LILAC paper.

For more information, please read the LILAC paper. README.md.html For questions and feedback, please open issues or email the corresponding author

Installation

git clone https://github.com/heejong-kim/lilac.git
cd lilac
# if you prefer to use install in conda env
conda create -n lilac python pip 
conda activate lilac
pip install -e .

Requirements

The LILAC package depends on the following requirements:

• torch
• torchvision>=0.11.2
• torchio>=0.18.73
• pandas>=1.3.4
• pillow>=8.4.0
• numpy>=1.21.2
• tensorboard

Running pip install -e . will automatically check for and install all of these requirements.

All baseline experiments in the paper were run on Python 3.9.7 and CUDA 11.2.

Examples

Learning to temporally order embryo images (LILAC-o)

python run.py \
    --jobname='embryo' \
    --task_option='o' \
    --targetname='phaseidx' \
    --backbone_name='cnn_2D' \
    --batchsize=64 \
    --max_epoch=40 \
    --output_directory='./output' \
    --image_directory='./embryo_image_location' \
    --image_size='224,224' \
    --csv_file_train='./demo_for_release/demo_embryo_train.csv' \
    --csv_file_val='./demo_for_release/demo_embryo_val.csv' \
    --csv_file_test='./demo_for_release/demo_embryo_test.csv'

Learning to temporally order wound-healing images (LILAC-o)

python run.py \
    --jobname='woundhealing' \
    --task_option='o' \
    --targetname='timepoint' \
    --backbone_name='cnn_2D' \
    --batchsize=128 \
    --max_epoch=40 \
    --output_directory='./output' \
    --image_directory='./woundhealing_image_location' \
    --image_size='224,224' \
    --csv_file_train='./demo_for_release/demo_woundhealing_train.csv' \
    --csv_file_val='./demo_for_release/demo_woundhealing_val.csv' \
    --csv_file_test='./demo_for_release/demo_woundhealing_test.csv'

Learning to predict temporal changes (LILAC-t)

python run.py \
    --jobname='oasis-aging' \
    --task_option='t' \
    --targetname='age' \
    --backbone_name='cnn_3D' \
    --batchsize=16 \
    --max_epoch=100 \
    --output_directory='./output' \
    --image_directory='./oasis-aging_image_location' \
    --image_size='128,128,128' \
    --csv_file_train='./demo_for_release/demo_oasis-aging_train.csv' \
    --csv_file_val='./demo_for_release/demo_oasis-aging_val.csv' \
    --csv_file_test='./demo_for_release/demo_oasis-aging_test.csv'

Learning to predict specific changes (LILAC-s)

python run.py \
    --jobname='adni-mci' \
    --task_option='s' \
    --targetname='CDRSB' \
    --optional_meta='AGExSEX'\
    --backbone_name='cnn_3D' \
    --batchsize=16 \
    --max_epoch=40 \
    --output_directory='./output' \
    --image_directory='./adni-mci_image_location' \
    --image_size='128,128,128' \
    --csv_file_train='./demo_for_release/demo_adni-mci_train.csv' \
    --csv_file_val='./demo_for_release/demo_adni-mci_val.csv' \
    --csv_file_test='./demo_for_release/demo_adni-mci_test.csv'

Inference

Simply adding --run_mode='eval' will run inference on the testing set specified in the CSV file provided through --csv_file_test=testset_list.csv. For example,

python run.py \
    --jobname='adni-mci' \
    --task_option='s' \
    --targetname='CDRSB' \
    --optional_meta='AGExSEX'\
    --backbone_name='cnn_3D' \
    --batchsize=16 \
    --max_epoch=40 \
    --output_directory='./output' \
    --image_directory='./adni-mci_image_location' \
    --image_size='128,128,128' \
    --csv_file_train='./demo_for_release/demo_adni-mci_train.csv' \
    --csv_file_val='./demo_for_release/demo_adni-mci_val.csv' \
    --csv_file_test='./demo_for_release/demo_adni-mci_test.csv'
    --run_mode='eval'

All *.csv files above are available in ./demo_for_release.