Adaptive Deep Multi-task Learning Framework for Image Segmentation (Cross Sectional and Longitudinal data analysis)

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Training/Cross-Validation/Test Data Sets

Running the GUI Program!

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:

Running the Program from the command line!

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]

Testing the Program (User Quick Start Guide)

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)