Abstract: A method includes obtaining, using at least one processing device of an electronic device, training images and captions. The method also includes preparing, using the at least one processing device, a training dataset based on the training images, the captions, and semantic knowledge of a large language model. In addition, the method includes training, using the at least one processing device, a machine learning model to determine entity categories and relations between entities in input images based on the training dataset, wherein the entities include subjects and objects captured in the input images.

Abstract: For heart segmentation in magnetic resonance or other medical imaging, deep learning trains a neural network. The neural network, such as U-net, includes at least one long-short-term memory (LSTM), such as a convolutional LSTM. The LSTM incorporates the temporal characteristics with the spatial to improve accuracy of the segmentation by the machine-learnt network.

Abstract: For heart segmentation in magnetic resonance or other medical imaging, deep learning trains a neural network. The neural network, such as U-net, includes at least one long-short-term memory (LSTM), such as a convolutional LSTM. The LSTM incorporates the temporal characteristics with the spatial to improve accuracy of the segmentation by the machine-learnt network.