Abstract: Systems and methods are provided for optimizing a deep learning model. A multi-site dataset associated with different clinical sites and a deployment dataset associated with a deployment clinical site are received. A deep learning model is trained based on the multi-site dataset. The trained deep learning model is optimized based on the deployment dataset. The optimized trained deep learning model is output.

Abstract: Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

Abstract: Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

Abstract: Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

Abstract: In an method for training artificial intelligence entities (AIE) for abnormality detection, medical imaging data of the human organ is provided as training data having training samples, the medical imaging data including imaging results from different types of imaging techniques for each training sample of the training data, a pre-trained or randomly initialized AIE is provided, and the AIE is trained using the provided training samples. The training may include, for at least one training sample, a first loss function for a sub-structure of the AIE is calculated independently of a first spatial region of the human organ, and, for a training sample, a second loss function for a sub-structure of the AIE is calculated independently of a second spatial region of the human organ. The AIE may be trained using the calculated first loss function and the calculated second loss function.

Abstract: Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

Abstract: Embodiments can provide a computer-implemented method for 3D motion correction for diffusion weighted imaging images, the method comprising acquiring a series of image slices from a medical imaging device; binning the series of image slices into bins, each bin comprising a plurality of slice locations; identifying, for each of the B-values, a dominating breathing state wherein at least one of the plurality of slice locations of the dominating breathing state contains an image slice from the series of images; identifying, for each of the B-values, one or more non-dominating breathing states; and registering, for each of the B-values, all of the one or more non-dominating breathing states to the dominating breathing state.

Abstract: Embodiments can provide a computer-implemented method for 3D motion correction for diffusion weighted imaging images, the method comprising acquiring a series of image slices from a medical imaging device; binning the series of image slices into bins, each bin comprising a plurality of slice locations; identifying, for each of the B-values, a dominating breathing state wherein at least one of the plurality of slice locations of the dominating breathing state contains an image slice from the series of images; identifying, for each of the B-values, one or more non-dominating breathing states; and registering, for each of the B-values, all of the one or more non-dominating breathing states to the dominating breathing state.