User-guided domain adaptation for rapid annotation from user interactions for pathological organ segmentation

A device for user-guided domain adaptation (UGDA), comprising:

• a memory, containing a computer program stored thereon;
• and
• a processor, coupled with the memory and configured, when the computer program being executed, to perform a method including:
• using a labelled source image dataset of medical images, training a combined model, including a first fully convolutional network (FCN) and a second FCN chained with the first FCN, by minimizing a supervised loss of the combined model to obtain one or more first sharing weights for the first FCN and one or more second sharing weights for the second FCN;
• using the combined model to generate extreme-point predictions, based on the one or more first sharing weights, and generate mask predictions, based on the one or more second sharing weights, for each of the source image dataset and a target image dataset;
• training a discriminator by inputting extreme-point/mask prediction pairs for each of the source image dataset and the target image dataset and by minimizing a discriminator loss to obtain one or more discriminator weights;
• and
• finetuning the combined model by predicting extreme-point/mask prediction pairs for the target image dataset to fool the discriminator having the one or more discriminator weights and matching a distribution of the extreme-point/mask prediction pairs for the target image dataset with a distribution of the extreme-point/mask prediction pairs of the source image dataset.

A computer program product comprising a non-transitory computer-readable storage medium and program instructions stored therein, the program instructions being configured to be executable by a computer to cause the computer to implement operations comprising:

• using a labelled source image dataset of medical images, training a combined model, including a first fully convolutional network (FCN) and a second FCN chained with the first FCN, by minimizing a supervised loss of the combined model to obtain one or more first sharing weights for the first FCN and one or more second sharing weights for the second FCN;
• using the combined model to generate extreme-point predictions, based on the one or more first sharing weights, and generate mask predictions, based on the one or more second sharing weights, for each of the source image dataset and a target image dataset;
• training a discriminator by inputting extreme-point/mask prediction pairs for each of the source image dataset and the target image dataset and by minimizing a discriminator loss to obtain one or more discriminator weights;
• and
• finetuning the combined model by predicting extreme-point/mask prediction pairs for the target image dataset to fool the discriminator having the one or more discriminator weights and matching a distribution of the extreme-point/mask prediction pairs for the target image dataset with a distribution of the extreme-point/mask prediction pairs of the source image dataset.