Abstract: A learning sub-system models search patterns of multiple experts in analyzing an image using a recurrent neural network (RNN) architecture, creates a knowledge base that models expert knowledge. A teaching sub-system teaches the search pattern captured by the RNN model and presents to a learning user the information for analyzing an image. The teaching sub-system determines the teaching image sequence based on a difficulty level identified using image features, audio cues, expert confidence and time taken by experts. An evaluation sub-system measures the learning user's performance in terms of search strategy that is evaluated against the RNN model and provides feedback on overall sequence followed by the learning user and time spent by the learning user on each region in the image.

Abstract: A learning sub-system models search patterns of multiple experts in analyzing an image using a recurrent neural network (RNN) architecture, creates a knowledge base that models expert knowledge. A teaching sub-system teaches the search pattern captured by the RNN model and presents to a learning user the information for analyzing an image. The teaching sub-system determines the teaching image sequence based on a difficulty level identified using image features, audio cues, expert confidence and time taken by experts. An evaluation sub-system measures the learning user's performance in terms of search strategy that is evaluated against the RNN model and provides feedback on overall sequence followed by the learning user and time spent by the learning user on each region in the image.

Abstract: Jointly determining image segmentation and characterization. A computer-generated image of an organ may be received. Organ characteristics estimation may be performed to predict the organ characteristics considering organ segmentation. Organ segmentation may be performed to delineate the organ in the image considering the organ characteristics. A feedback loop feeds the organ characteristics estimation to determine the organ segmentation, and feeds back the organ segmentation to determine the organ characteristics estimation.

Abstract: A learning sub-system models search patterns of multiple experts in analyzing an image using a recurrent neural network (RNN) architecture, creates a knowledge base that models expert knowledge. A teaching sub-system teaches the search pattern captured by the RNN model and presents to a learning user the information for analyzing an image. The teaching sub-system determines the teaching image sequence based on a difficulty level identified using image features, audio cues, expert confidence and time taken by experts. An evaluation sub-system measures the learning user's performance in terms of search strategy that is evaluated against the RNN model and provides feedback on overall sequence followed by the learning user and time spent by the learning user on each region in the image.

Abstract: A learning sub-system models search patterns of multiple experts in analyzing an image using a recurrent neural network (RNN) architecture, creates a knowledge base that models expert knowledge. A teaching sub-system teaches the search pattern captured by the RNN model and presents to a learning user the information for analyzing an image. The teaching sub-system determines the teaching image sequence based on a difficulty level identified using image features, audio cues, expert confidence and time taken by experts. An evaluation sub-system measures the learning user's performance in terms of search strategy that is evaluated against the RNN model and provides feedback on overall sequence followed by the learning user and time spent by the learning user on each region in the image.

Abstract: Automatically determining image quality of a machine generated image may generate a local saliency map of the image to obtain a set of unsupervised features. The image is run through a trained convolutional neural network (CNN) to extract a set of supervised features from a fully connected layer of the CNN, the image convolved with a set of learned kernels from the CNN to obtain a complementary set of supervised features. The set of unsupervised features and the complementary set of supervised features are combined, and a first decision on gradability of the image is predicted. A second decision on gradability of the image is predicted based on the set of supervised features. Whether the image is gradable is determined based on a weighted combination of the first decision and the second decision.

Abstract: Jointly determining image segmentation and characterization. A computer-generated image of an organ may be received. Organ characteristics estimation may be performed to predict the organ characteristics considering organ segmentation. Organ segmentation may be performed to delineate the organ in the image considering the organ characteristics. A feedback loop feeds the organ characteristics estimation to determine the organ segmentation, and feeds back the organ segmentation to determine the organ characteristics estimation.

Abstract: Automatically determining image quality of a machine generated image may generate a local saliency map of the image to obtain a set of unsupervised features. The image is run through a trained convolutional neural network (CNN) to extract a set of supervised features from a fully connected layer of the CNN, the image convolved with a set of learned kernels from the CNN to obtain a complementary set of supervised features. The set of unsupervised features and the complementary set of supervised features are combined, and a first decision on gradability of the image is predicted. A second decision on gradability of the image is predicted based on the set of supervised features. Whether the image is gradable is determined based on a weighted combination of the first decision and the second decision.