Based on the tutorial of 2D registration

Download the dataset from here and extract the contents to a convenient location.

The MedNIST dataset was gathered from several sets from TCIA, the RSNA Bone Age Challenge, and the NIH Chest X-ray dataset.

The dataset is kindly made available by Dr. Bradley J. Erickson M.D., Ph.D. (Department of Radiology, Mayo Clinic) under the Creative Commons CC BY-SA 4.0 license.

If you use the MedNIST dataset, please acknowledge the source.

Training with same-subject image inputs

Training with cross-subject image inputs

Training from an existing checkpoint file, for example, models/model_key_metric=-0.0734.pt:

The following figure shows an intra-subject (--cross_subjects False) model inference results (Fixed, moving and predicted images from left to right)

The command shows an inference workflow with the checkpoint "models/model_key_metric=-0.0890.pt" and using device "cuda:1":

The following commands starts a finetuning workflow based on the checkpoint "models/model_key_metric=-0.0065.pt" for 5 epochs using the global mutual information loss.

The following figure shows an inter-subject (--cross_subjects True) model inference results (Fixed, moving and predicted images from left to right)

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