Abstract: An example computer-implemented method for self-supervised training of an image processing model for histopathology images is provided. The example method includes obtaining a reference histopathology image; generating an augmented histopathology image, wherein generating the augmented histopathology image comprises performing, for an input image, at least one of the following augmentations: applying a blur to the input image and injecting noise artifacts into the blurred input image; or cropping a plurality of portions from the input image, wherein the plurality of portions are determined based on a minimum overlap criterion that has been updated over one or more iterations; and training the image processing model based on a similarity of latent representations generated by the image processing model respectively for the reference histopathology image and the augmented histopathology image.

Abstract: Apparatus and method for load balancing communication contacts between a plurality of remote, non-associated contact centres in a commercial context. A centralised processing hub is configured to receive and route communication traffic between contact centres based on a number of specific contact routing conditions. A network is configured between contact centres and the central processing hub which manages routing of contact interactions between contact centres utilising a service by which data relevant to the routed contact is presented and by which data may be captured, processed and transferred. Contact centre specific data may be shared with the central hub to enable more efficient resource management in the face of fluctuations in in-bound contact traffic. The central processing hub provides a centralised market infrastructure in which contact resource capacity may be traded under a buy and sell scenario.