Abstract: Broadly speaking, embodiments of the present techniques provide a method and system for personalising machine learning models on resource-constrained devices by using conditional neural networks. In particular, the present techniques allow for resource-efficient use of a conditioning vector by incorporating the conditioning vector into weights learned during training. This reduces the computational resources required at inference time.

Abstract: Broadly speaking, the present techniques provide methods for conditioning a neural network, which not only improve the generalizable performance of conditional neural networks, but also reduce model size and latency significantly. The resulting conditioned neural network is suitable for on-device deployment due to having a significantly lower model size, lower dynamic memory requirement, and lower latency.

Abstract: The disclosure relates to methods, apparatuses and systems for improving a neural architecture search (NAS). For example, A computer-implemented method using a searching algorithm to design a neural network architecture is provided, the method including: obtaining a plurality of neural network models; selecting a first subset of the plurality of neural network models; applying the searching algorithm to the selected subset of models; and identifying an optimal neural network architecture by repeating the selecting and applying for a fixed number of iterations; wherein at least one score indicative of validation loss for each model is used in or alongside at least one of the selecting and applying.

Abstract: An apparatus for processing a medical image includes an image processor including a plurality of processors, the plurality of processors configured to reconstruct a cross-sectional image of an object by performing a first operation having a first priority and a second operation having a second priority that is lower than the first priority, and a controller configured to monitor whether a malfunction occurs among the plurality of processors, and configured to assign, to at least one of the plurality of processors, at least one of the first operation and the second operation to be performed, based on a result of monitoring of the plurality of processors.

Abstract: An apparatus for processing a medical image includes an image processor including a plurality of processors, the plurality of processors configured to reconstruct a cross-sectional image of an object by performing a first operation having a first priority and a second operation having a second priority that is lower than the first priority, and a controller configured to monitor whether a malfunction occurs among the plurality of processors, and configured to assign, to at least one of the plurality of processors, at least one of the first operation and the second operation to be performed, based on a result of monitoring of the plurality of processors.

Abstract: An apparatus for processing a medical image includes an image processor including a plurality of processors, the plurality of processors configured to reconstruct a cross-sectional image of an object by performing a first operation having a first priority and a second operation having a second priority that is lower than the first priority, and a controller configured to monitor whether a malfunction occurs among the plurality of processors, and configured to assign, to at least one of the plurality of processors, at least one of the first operation and the second operation to be performed, based on a result of monitoring of the plurality of processors.

Abstract: A radiographic imaging apparatus includes a radiation scanner; and a workstation configured to control the radiation scanner. The workstation is configured to output analyzed data for a functional error of at least one of the radiation scanner and the workstation in graphics, and output analyzed data for an item of the functional error in response to an input of a user.

Abstract: An apparatus for processing a medical image includes an image processor including a plurality of processors, the plurality of processors configured to reconstruct a cross-sectional image of an object by performing a first operation having a first priority and a second operation having a second priority that is lower than the first priority, and a controller configured to monitor whether a malfunction occurs among the plurality of processors, and configured to assign, to at least one of the plurality of processors, at least one of the first operation and the second operation to be performed, based on a result of monitoring of the plurality of processors.