3. Install packages for mpMRI-based PLDC testing.
- python=3.6.5
- Keras
- Tensorflow = 1.15
- Opencv-python
- Pydicom
- Numpy
- Pillow
- Scikit-image
- SimpleITK
4. In order to perform PLDC in local cohort samples, you should train a domain adaptation (DA) model first (see details in the next Section " Prostate lesion assessment using your local cohort mpMRI "). Put all of your well-trained model weights under ./PLDC_software/doc/weights/. Then, you can test your samples using the open-sourced system with your well-trained DA model.
5. Begin to test your target mpMRIs. Prepare your local cohort samples, and open the executable software. Start your testing via "Main menu" → "Start testing". The predicted results will be under the folder "./media/output/", including prostate segmentation, prostate lesion detection, and lesion malignancy results. You can download the following prostate_exe.mp4 to learn the details if necessary.