Adaptive Deep Multi-task Learning Framework for Image Segmentation (Cross Sectional and Longitudinal data analysis)
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First, user needs to install Anaconda https://www.anaconda.com/
Then
CPU run
- conda env create -f cnn_run_conda_environment_cpu.yml
GPU run
- conda env create -f cnn_run_conda_environment_gpu.yml
and
CPU run
- conda activate idptfcpu
GPU run
- conda activate tf-gpu
finally
- python CNN_Longitudinal_CrossSectional_GUI.py
After lunching the graphical user interface, user will need to provide necessary information to start training/testing as follows:
First
CPU run
- conda activate idptfcpu
GPU run
- conda activate tf-gpu
then for training
- python -m tbb training_script_Cross_Sectional.py [or training_script_Longitudinal.py]
for testing
- python -m tbb inference_script_Cross_Sectional.py [or inference_script_Longitudinal.py]
Examples of Training, Cross-validation and Testing subjects can be found in: https://github.com/kbronik2017/UCL_MS/tree/master/examples/mnist_dataset (which will allow users to quickly and easily train and test the program)