Closed qiyedongcode closed 1 year ago
We have already provided demos for the semi-supervised learning methods. See: https://github.com/HiLab-git/PyMIC_examples/tree/main/seg_ssl/ACDC
To use your own data, just set "root_dir" and the "train_csv", "train_csv_unlab", "valid_csv" and "test_csv" path in the configuration file, such as following:
tensor_type = float task_type = seg root_dir = ../../PyMIC_data/ACDC/preprocess/ train_csv = config/data/image_train_r10_lab.csv train_csv_unlab = config/data/image_train_r10_unlab.csv valid_csv = config/data/image_valid.csv test_csv = config/data/image_test.csv
Thank you for your reply, can you provide some cfg files for semi-supervised learning under 3D networks and preprocessing methods for ACDC datasets
Thanks for your suggestion. However, the ACDC dataset has a large inter-slice spacing and it is more suitable for using 2D networks. We will consider to provide a 3D semi-supervised segmentation demo in the following.
Really exciting work! I would like to know how to use your PyMIC to complete the semi-supervised learning of my own CT data, can you make a demo?