MVA-DLMI

Deep Learning for Medical Imaging Khaoula Belahsen - Nadir El Manouzi

Data

The images from the training set and the test set are in the folders train and test.

The first cells are used to create paths to npy files which will be created in the next cell with the functions create_train_data() and create_test_data().

create_augmented_train_data() can be used to create the augmented data set.

Pre-trained models

Pre-trained models are available in the folder res

without data augmentation : unet_2heads.hdf5, r2unet.hdf5, unet_classic.hdf5, Nestnet.hdf5 and AttResNet.hdf5

with data augmentation : unet_2heads_dataaug_rmsprop.hdf5 and nestnet_aug.hdf5

In the model choice part of the code, uncomment the model of your choice, put data_augmentation = False or data_augmentation = True depending on the dataset you want to use and uncomment the pre-trained path corresponding to the model chosen.

Training

In the model choice part of the code, uncomment the model of your choice, put data_augmentation = False or data_augmentation = True depending of the dataset you want to use and uncomment pretrained_path = None

Submission

For the U-Net with 2 heads, use submission(two_outputs=True) For all the other models, use submission(two_outputs=False)

A file submission.csv will then be generated.

Qualitative results

In order to have qualititative results on 3 different images, just run the last cell of the notebook.