Abstract: A management system operates in conjunction with entities to provide component recommendations for objects. The management system trains a machine learning model used to generate the component recommendations. The machine learning model is trained based on historical component entries describing components previously provided and identifiers of the components. The management system generates training data by classifying the historical component entries into predetermined component classifications. After the machine learning model is trained, the management system generates a customized recommendation of components for an object based on likelihoods of selection of the predetermined component classifications.

Abstract: A management system operates in conjunction with entities to provide component recommendations for objects. The management system trains a machine learning model used to generate the component recommendations. The machine learning model is trained based on historical component entries describing components previously provided and identifiers of the components. The management system generates training data by classifying the historical component entries into predetermined component classifications. After the machine learning model is trained, the management system generates a customized recommendation of components for an object based on likelihoods of selection of the predetermined component classifications.

Abstract: A management system operates in conjunction with entities to provide component recommendations for objects. The management system trains a machine learning model used to generate the component recommendations. The machine learning model is trained based on historical component entries describing components previously provided and identifiers of the components. The management system generates training data by classifying the historical component entries into predetermined component classifications. After the machine learning model is trained, the management system generates a customized recommendation of components for an object based on likelihoods of selection of the predetermined component classifications.

Abstract: Disclosed herein is a method of medical imaging. The method comprises: receiving (200) an echo planar diffusion weighted magnetic resonance image (122) of a region of interest (309) descriptive of breast tissue; receiving (202) a fat suppressed T2 weighted magnetic resonance image (124) descriptive of the region of interest; segmenting (204) the echo planar diffusion weighted magnetic resonance image to identify high diffusion rate regions (128); segmenting (206) the fat suppressed T2 weighted magnetic resonance image to identify tissue regions (130); identifying (208) a portion of the tissue regions as advisory regions (134) by inputting the high diffusion rate regions and the tissue regions into an image processing module; and providing (210) the advisory regions as a segmentation of the fat suppressed T2 weighted magnetic resonance image.

Abstract: A management system operates in conjunction with entities to provide component recommendations for objects. The management system trains a machine learning model used to generate the component recommendations. The machine learning model is trained based on historical component entries describing components previously provided and identifiers of the components. The management system generates training data by classifying the historical component entries into predetermined component classifications. After the machine learning model is trained, the management system generates a customized recommendation of components for an object based on likelihoods of selection of the predetermined component classifications.