Abstract: A method for digital image classification and localization includes receiving a digital image of a biological organism from an imaging apparatus, the digital image comprising a plurality of intensities on a 2-dimensional grid of points, generating a plurality of discriminative representations of the 2D digital image by extracting dominant characteristics of the image from three different viewpoints, where the plurality of discriminative representations form a 3-dimensional digital image, combining the 3D digital image with the 2D digital image in a convolutional neural network that outputs a 3-channel feature map that localizes image abnormalities in each of the three channels and includes a detection confidence that each abnormalities is a neoplasm, providing the 3-channel feature map to a controller of a robotic surgical device where the robotic surgical device uses the 3-channel feature map to locate the neoplasm within the biological organism in a surgical procedure for treating the neoplasm.

Abstract: A method for digital image classification and localization includes receiving a digital image of a biological organism from an imaging apparatus, the digital image comprising a plurality of intensities on a 2-dimensional grid of points, generating a plurality of discriminative representations of the 2D digital image by extracting dominant characteristics of the image from three different viewpoints, where the plurality of discriminative representations form a 3-dimensional digital image, combining the 3D digital image with the 2D digital image in a convolutional neural network that outputs a 3-channel feature map that localizes image abnormalities in each of the three channels and includes a detection confidence that each abnormalities is a neoplasm, providing the 3-channel feature map to a controller of a robotic surgical device where the robotic surgical device uses the 3-channel feature map to locate the neoplasm within the biological organism in a surgical procedure for treating the neoplasm.