Abstract: A computer-implemented screening method, system, and computer program product including processing a first sensor scan image at a first time and at a first location to detect a first disassembled component of an object, processing a second sensor scan image at a second time and at a second location to detect a second disassembled component of the object, storing a record of the first disassembled component and the second disassembled component, and flagging the record based on a risk factor of the first disassembled component being combined with the second disassembled component to complete the object.

Abstract: A computer-implemented screening method, system, and computer program product including detecting at least two disassembled components of an object spread across multiple sensor scan images of at least one container, storing a record of each disassembled component correlated with contextual information of each owner of each container corresponding to a sensor scan image, and flagging the owner of the container in the sensor scan image based on a joint assessment of the sensor scan image including a disassembled component that can be combined with another disassembled component in a different sensor scan image to assemble the object and the contextual information of each owner.

Abstract: A system for registering and segmenting images includes an image scanner configured to acquire an image pair including a first image at a first time and a second image at a second time that is after the first time. A joint registration and segmentation server receives the image pair from the image scanner and simultaneously performs joint registration and segmentation on the image pair using a single deep learning framework. A computer vision processor receives an output of the joint registration and segmentation server and characterizes how a condition has progressed from the first time to the second time therefrom. A user terminal presents the characterization to a user.

Abstract: A system for registering and segmenting images includes an image scanner configured to acquire an image pair including a first image at a first time and a second image at a second time that is after the first time. A joint registration and segmentation server receives the image pair from the image scanner and simultaneously performs joint registration and segmentation on the image pair using a single deep learning framework. A computer vision processor receives an output of the joint registration and segmentation server and characterizes how a condition has progressed from the first time to the second time therefrom. A user terminal presents the characterization to a user.

Abstract: A method for computer-aided diagnosis of skin lesions includes obtaining a dermoscopic image, convolving the dermoscopic image in a plurality of convolutional layers, obtaining deconvolved outputs of at least two convolutional layers of the plurality of convolutional layers, obtaining side-output feature maps by applying loss functions to the deconvolved outputs of the at least two convolutional layers, obtaining a first concatenated feature map by concatenating the side-output feature maps with different first weights, obtaining a second concatenated feature map by concatenating the side-output feature maps with different second weights, and producing a final score map by convolving the first and second concatenated feature maps in a final convolutional layer followed by a loss layer. Also disclosed: a computer-readable medium embodying instructions for the method, and an apparatus configured to implement the method.

Abstract: A computer-implemented screening method, system, and computer program product including detecting at least two disassembled components of an object spread across multiple sensor scan images of at least one container, storing a record of each disassembled component correlated with contextual information of each owner of each container corresponding to a sensor scan image, and flagging the owner of the container in the sensor scan image based on a joint assessment of the sensor scan image including a disassembled component that can be combined with another disassembled component in a different sensor scan image to assemble the object and the contextual information of each owner.

Abstract: A method for computer-aided diagnosis of skin lesions includes obtaining a dermoscopic image, convolving the dermoscopic image in a plurality of convolutional layers, obtaining deconvolved outputs of at least two convolutional layers of the plurality of convolutional layers, obtaining side-output feature maps by applying loss functions to the deconvolved outputs of the at least two convolutional layers, obtaining a first concatenated feature map by concatenating the side-output feature maps with different first weights, obtaining a second concatenated feature map by concatenating the side-output feature maps with different second weights, and producing a final score map by convolving the first and second concatenated feature maps in a final convolutional layer followed by a loss layer. Also disclosed: a computer-readable medium embodying instructions for the method, and an apparatus configured to implement the method.