Abstract: A mechanism is provided in a data processing system for automatic determination of b-value difference from diffusion-weighted (DW) images. The mechanism receives a series of images wherein a first image has a first b-value and a second image has an unknown b-value. The mechanism applies a generative adversarial network (GAN) model to estimate a difference between b-values in the series of images. The mechanism determines a b-value for the second image based on the first b-value and the estimated difference between b-values.

Abstract: A mechanism is provided in a data processing system for automatic determination of b-value difference from diffusion-weighted (DW) images. The mechanism receives a series of images wherein a first image has a first b-value and a second image has an unknown b-value. The mechanism applies a generative adversarial network (GAN) model to estimate a difference between b-values in the series of images. The mechanism determines a b-value for the second image based on the first b-value and the estimated difference between b-values.

Abstract: In an approach for image registration performance assurance by optimizing system configurations, a processor evaluates alignment of a registered image and a fixed image using a pre-trained learning model. The registered image is generated with a first registration method. A processor provides a reward score to the alignment, the reward score being defined as a higher score indicating a better alignment. A processor generates a registration status represented as a feature vector that contains information about how the registered and fixed images are aligned. A processor determines a second registration method based on the reward score, the feature vector, and the first registration method.

Abstract: Some embodiments of the present invention select image data that are valuable for developing and/or training a deep learning based algorithm. A semi-automated system identifies cases that are the most valuable (most impactful, useful, and/or most effective) for developing and/or training the deep learning algorithm. The semi-automated system monitors a degree of uncertainty in the results produced by an image processing algorithm. Cases where the degree of uncertainty is high, and consequently a confidence score is low, are made ready for analysis, classification, and/or annotation by human review. Once analyzed, classified and/or annotated by human review, the data is made available for use in developing and/or training the deep learning algorithm.

Abstract: Some embodiments of the present invention select image data that are valuable for developing and/or training a deep learning based algorithm. A semi-automated system identifies cases that are the most valuable (most impactful, useful, and/or most effective) for developing and/or training the deep learning algorithm. The semi-automated system monitors a degree of uncertainty in the results produced by an image processing algorithm. Cases where the degree of uncertainty is high, and consequently a confidence score is low, are made ready for analysis, classification, and/or annotation by human review. Once analyzed, classified and/or annotated by human review, the data is made available for use in developing and/or training the deep learning algorithm.