Abstract: A method operating a Graphics Processing Unit (GPU) memory can be provided by accessing specified training parameters used to train a Deep Neural Network (DNN) using a GPU with a local GPU memory, the specified training parameters including at least a specified batch size of samples configured to train the DNN. A sub-batch size of the samples can be defined that is less than or equal to the specified batch size of samples in response to determining that an available size of the local GPU memory is insufficient to store all data associated with training the DNN using one batch of the samples. Instructions configured to train the DNN using the sub-batch size can be defined so that an accuracy of the DNN trained using the sub-batch size is about equal to an accuracy of the DNN trained using the specified batch size of the samples.

Abstract: Optimizations are provided for segmenting tissue objects included in an ultrasound image. Initially, raw pixel data is received. Here, each pixel corresponds to ultrasound information. This raw pixel data is processed through a first fully convolutional network to generate a first segmentation label map. This first map includes a first set of objects that have been segmented into a coarse segmentation class. Then, this first map is processed through a second fully convolutional network to generate a second segmentation label map. This second map is processed using the raw pixel data as a base reference. Further, this second map includes a second set of objects that have been segmented into a fine segmentation class. Then, a contour optimization algorithm is applied to at least one of the second set of objects in order to refine that object's contour boundary. Subsequently, that object is identified as corresponding to a lymph node.

Abstract: A method operating a Graphics Processing Unit (GPU) memory can be provided by accessing specified training parameters used to train a Deep Neural Network (DNN) using a GPU with a local GPU memory, the specified training parameters including at least a specified batch size of samples configured to train the DNN. A sub-batch size of the samples can be defined that is less than or equal to the specified batch size of samples in response to determining that an available size of the local GPU memory is insufficient to store all data associated with training the DNN using one batch of the samples. Instructions configured to train the DNN using the sub-batch size can be defined so that an accuracy of the DNN trained using the sub-batch size is about equal to an accuracy of the DNN trained using the specified batch size of the samples.

Abstract: Optimizations are provided for segmenting tissue objects included in an ultrasound image. Initially, raw pixel data is received. Here, each pixel corresponds to ultrasound information. This raw pixel data is processed through a first fully convolutional network to generate a first segmentation label map. This first map includes a first set of objects that have been segmented into a coarse segmentation class. Then, this first map is processed through a second fully convolutional network to generate a second segmentation label map. This second map is processed using the raw pixel data as a base reference. Further, this second map includes a second set of objects that have been segmented into a fine segmentation class. Then, a contour optimization algorithm is applied to at least one of the second set of objects in order to refine that object's contour boundary. Subsequently, that object is identified as corresponding to a lymph node.

Abstract: Systems and methods are provided for identifying markers for inflammation in a tissue image. The tissue image is captured as an image of a histology slide. Subcellular structures in the tissue image are segmented via a first automated process to identify at least one variety of immune cells within the image. Glands and vilii are identified within the tissue image via a second automated process. Neutrophils are identified within the tissue image via a third automated process. An output representing the identified glands, villi, neutrophils, and other immune cells is provided to a human operator.

Abstract: Systems and methods are provided for identifying markers for inflammation in a tissue image. The tissue image is captured as an image of a histology slide. Subcellular structures in the tissue image are segmented via a first automated process to identify at least one variety of immune cells within the image. Glands and vilii are identified within the tissue image via a second automated process. Neutrophils are identified within the tissue image via a third automated process. An output representing the identified glands, villi, neutrophils, and other immune cells is provided to a human operator.

Abstract: Disclosed is an example method to calculate radiation dose. The method includes receiving a tissue matrix in which the tissue matrix includes a plurality of voxels. The example method also includes producing a first plurality of transport lines with a direction controller in which each transport line is indicative of a cone of irradiated energy, and calculating at least one radiation dose with at least one deposit engine substantially in parallel with producing a second plurality of transport lines with the direction controller.

Abstract: Disclosed is an example method to calculate radiation dose. The method includes receiving a tissue matrix in which the tissue matrix includes a plurality of voxels. The example method also includes producing a first plurality of transport lines with a direction controller in which each transport line is indicative of a cone of irradiated energy, and calculating at least one radiation dose with at least one deposit engine substantially in parallel with producing a second plurality of transport lines with the direction controller.