Abstract: A method for computer vision processing. The method includes projecting input visual data into a plurality of intermediate feature maps by performing a plurality of 1×1 convolution operations; generating an attention weighted map by performing attention and aggregation operations on the plurality of intermediate feature maps; generating a convolved feature map by performing shift and summation operations on the plurality of intermediate feature maps; and adding the attention weighted map and the convolved feature map based on at least one scalar.

Abstract: Examples relate to an ophthalmic microscope system and to a corresponding system, method and computer program for an ophthalmic microscope system. The system comprises one or more processors and one or more storage devices. The system is configured to obtain intraoperative sensor data of an eye from at least one imaging device of the ophthalmic microscope system. The system is configured to process the intraoperative sensor data using a machine-learning model. The machine-learning model is trained to output information on one or more anatomical features of the eye based on the intraoperative sensor data. The system is configured to generate a display signal for a display device of the ophthalmic microscope system based on the information on the one or more anatomical features of the eye. The display signal comprises a visual guidance overlay for guiding a user of the ophthalmic microscope system with respect to the one or more anatomical features of the eye.

Abstract: An apparatus for processing image data of tissue that includes at least one processor is disclosed. The apparatus is configured for determining a profile from a plurality of profiles, and applying a plurality of settings from the determined profile. The apparatus determines the image based on the applied settings, and outputs data for displaying at least one image. Each profile includes a plurality of settings. Each profile can be configured to enhance and/or suppress anatomical features of the image.

Abstract: API gateway log data of HTTP response information is collected and aggregated for each API service. The log data is streamed and formed into a feature vector. A model training module trains a neural network model based on the feature vector and model metadata. The model training module also retrains the neural network model based on a new feature vector. Neural network model artifacts are stored and used to detect anomalous patterns and generate alerts of the detection.

Abstract: API gateway log data of HTTP response information is collected and aggregated for each API service. The log data is streamed and formed into a feature vector. A model training module trains a neural network model based on the feature vector and model metadata. The model training module also retrains the neural network model based on a new feature vector. Neural network model artifacts are stored and used to detect anomalous patterns and generate alerts of the detection.