Abstract: The present disclosure provides a computerized method for a likelihood of malignancy in breast tissue of a patient. The method includes receiving, with a computer processor, an image of the breast tissue, providing the image of the breast tissue to a model including a trained neural network; the trained neural network being previously trained by training a first neural network, initializing a second neural network based on the first neural network, training the second neural network, and outputting the second neural network as the trained neural network, receiving an indicator from the model, and outputting a report including the indicator to at least one of a memory or a display.

Abstract: The present disclosure provides a method for determining a malignancy likelihood score for breast tissue of a patient. The method includes receiving a plurality of two-dimensional images of the breast tissue, the two-dimensional images being derived from a three-dimensional image of the breast tissue, for each two-dimensional image, providing the two-dimensional image to a first model including a first trained neural network, and receiving a number of indicators from the first model, each indicator being associated with a two-dimensional image included in the plurality of two-dimensional images, generating a synthetic two-dimensional image based on the number of indicators and at least one of the plurality of two-dimensional images, providing the synthetic two-dimensional image to a second model including a second trained neural network, receiving a malignancy likelihood score from the second model, and outputting a report including the malignancy likelihood score to at least one of a memory or a display.

Abstract: The present disclosure provides a computerized method for a likelihood of malignancy in breast tissue of a patient. The method includes receiving, with a computer processor, an image of the breast tissue, providing the image of the breast tissue to a model including a trained neural network; the trained neural network being previously trained by training a first neural network, initializing a second neural network based on the first neural network, training the second neural network, and outputting the second neural network as the trained neural network, receiving an indicator from the model, and outputting a report including the indicator to at least one of a memory or a display.

Abstract: The present disclosure provides a method for determining a malignancy likelihood score for breast tissue of a patient. The method includes receiving a plurality of two-dimensional images of the breast tissue, the two-dimensional images being derived from a three-dimensional image of the breast tissue, for each two-dimensional image, providing the two-dimensional image to a first model including a first trained neural network, and receiving a number of indicators from the first model, each indicator being associated with a two-dimensional image included in the plurality of two-dimensional images, generating a synthetic two-dimensional image based on the number of indicators and at least one of the plurality of two-dimensional images, providing the synthetic two-dimensional image to a second model including a second trained neural network, receiving a malignancy likelihood score from the second model, and outputting a report including the malignancy likelihood score to at least one of a memory or a display.