Abstract: In some embodiments, a method includes sending, from a first set of computing devices, a distributed instance of a machine learning model to a client computing device, where the client computing device is caused to provide a set of outputs related to the noise data, and where the set of outputs is an output of the distributed instance derived from inputting the noise data into the distributed instance. The method further includes receiving the set of outputs from the client computing device and configuring another instance of the machine learning model based on the noise data and the set of outputs related to the noise data.

Abstract: Methods and systems are disclosed for improved operation of applications through user interfaces. In some embodiments, the methods and systems relate to training an artificial neural network to complete a task within an application by mimicking and emulating interactions of human operators with the application interface.

Abstract: In some embodiments, noise data may be used to train a neural network (or other prediction model). In some embodiments, input noise data may be obtained and provided to a prediction model to obtain an output related to the input noise data (e.g., the output being a prediction related to the input noise data). One or more target output indications may be provided as reference feedback to the prediction model to update one or more portions of the prediction model, wherein the one or more portions of the prediction model are updated based on the related output and the target indications. Subsequent to the portions of the prediction model being updated, a data item may be provided to the prediction model to obtain a prediction related to the data item (e.g., a different version of the data item, a location of an aspect in the data item, etc.).

Abstract: The present disclosure pertains to autonomous control of an imaging system. In some embodiments, training information including at least a plurality of images and action information are received. The plurality of images and action information are provided to a prediction model to train the prediction model. Further, an image capturing device is controlled to capture an image of a portion of a living organism, the image is processed, via the prediction model, to determine an action to be taken with respect to the image, and the determined action is taken with respect to the image.

Abstract: The present disclosure pertains to autonomous control of an imaging system. In some embodiments, training information including at least a plurality of images and action information are received. The plurality of images and action information are provided to a prediction model to train the prediction model. Further, an image capturing device is controlled to capture an image of a portion of a living organism, the image is processed, via the prediction model, to determine an action to be taken with respect to the image, and the determined action is taken with respect to the image.

Abstract: The present disclosure pertains to autonomous control of an imaging system. In some embodiments, training information including at least a plurality of images and action information are received. The plurality of images and action information are provided to a prediction model to train the prediction model. Further, an image capturing device is controlled to capture an image of a portion of a living organism, the image is processed, via the prediction model, to determine an action to be taken with respect to the image, and the determined action is taken with respect to the image.

Abstract: In some embodiments, noise data may be used to train a neural network (or other prediction model). In some embodiments, input noise data may be obtained and provided to a prediction model to obtain an output related to the input noise data (e.g., the output being a prediction related to the input noise data). One or more target output indications may be provided as reference feedback to the prediction model to update one or more portions of the prediction model, wherein the one or more portions of the prediction model are updated based on the related output and the target indications. Subsequent to the portions of the prediction model being updated, a data item may be provided to the prediction model to obtain a prediction related to the data item (e.g., a different version of the data item, a location of an aspect in the data item, etc.).

Abstract: Methods and systems are disclosed for improved operation of applications through user interfaces. In some embodiments, the methods and systems relate to training an artificial neural network to complete a task within an application by mimicking and emulating interactions of human operators with the application interface.

Abstract: Methods and systems are disclosed for improved operation of applications through user interfaces. In some embodiments, the methods and systems relate to training an artificial neural network to complete a task within an application by mimicking and emulating interactions of human operators with the application interface.

Abstract: In some embodiments, noise data may be used to train a neural network (or other prediction model). In some embodiments, input noise data may be obtained and provided to a prediction model to obtain an output related to the input noise data (e.g., the output being a prediction related to the input noise data). One or more target output indications may be provided as reference feedback to the prediction model to update one or more portions of the prediction model, wherein the one or more portions of the prediction model are updated based on the related output and the target indications. Subsequent to the portions of the prediction model being updated, a data item may be provided to the prediction model to obtain a prediction related to the data item (e.g., a different version of the data item, a location of an aspect in the data item, etc.).

Abstract: Apparatuses, methods, and systems for an ablation device (e.g., for catheter-based ablation procedures) that simplifies the invasive treatment of atrial fibrillation by allowing a medical professional to detect both force and pressure during the procedure. For example, using a sensor affixed to the ablation device, a medical professional may detect (or infer) both the force applied to an organ during an ablation procedure as well as the direction of that force. The detected force and direction applied against the surrounding tissue may then be communicated (e.g., using text, on-screen graphics, sounds, etc.) to a medical professional though a display unit connected to the ablation device.

Abstract: An ablation catheter comprises an elongate catheter body and a branch electrode element. The elongate catheter body has a central axis, a distal end, and a proximal end. The elongate catheter body is configured to be steered within a heart chamber. The branch electrode element has a base end and a working end or tip. The base end is secured to the elongate catheter body at a location spaced proximally of the distal end and an effector on the working end. The branch electrode element is configured to evert when the distal end of the elongate catheter body is in a pulmonary vein ostium or os so that the effector can be selectively engaged against locations surrounding the pulmonary vein os which differ in radial direction and distance. One or more of linear or curved ablation patterns are generated on tissue adjacent the pulmonary vein os with the effector.

Abstract: Methods and systems are disclosed for improved operation of applications through user interfaces. In some embodiments, the methods and systems relate to training an artificial neural network to complete a task within an application by mimicking and emulating interactions of human operators with the application interface.

Abstract: Methods and systems are described for autonomous control of imaging devices. In particular, the methods and system described herein may account for the differences in normalization of training data and/or test data. The methods and systems may process images through an additional customization layer, which itself may comprise an artificial neural network. The additional customization layer is trained to normalize data for specific applications and/or differences between subsets of data.

Abstract: In certain embodiments, a coil circuitry component may be configured to detect RF signals from excited spins of at least a region of an organism, where the coil circuitry component comprises a RF detection coil and a detuning circuit for detuning the RF detection coil. A coil signal detection component may be configured to extract at least some of the RF signals detected by the coil circuitry component and to convert the extracted RF signals from analog signal to digital signals. An excitation estimation component may be configured to estimate the excitation pulses from an excitation source and to generate a control timing signal from the estimated excitation pulses to set a state of the detuning circuit. A wireless communication component may be configured to wirelessly transmit the converted RF signals, the estimated excitation pulses, and the control timing signal to an external computer system.

Abstract: In some embodiments, noise data may be used to train a neural network (or other prediction model). In some embodiments, input noise data may be obtained and provided to a prediction model to obtain an output related to the input noise data (e.g., the output being a prediction related to the input noise data). One or more target output indications may be provided as reference feedback to the prediction model to update one or more portions of the prediction model, wherein the one or more portions of the prediction model are updated based on the related output and the target indications. Subsequent to the portions of the prediction model being updated, a data item may be provided to the prediction model to obtain a prediction related to the data item (e.g., a different version of the data item, a location of an aspect in the data item, etc.).

Abstract: Methods and systems are described for autonomous control of imaging devices. In particular, the methods and system described herein may account for the differences in normalization of training data and/or test data. The methods and systems may process images through an additional customization layer, which itself may comprise an artificial neural network. The additional customization layer is trained to normalize data for specific applications and/or differences between subsets of data.

Abstract: In some embodiments, noise data may be used to train a neural network (or other prediction model). In some embodiments, input noise data may be obtained and provided to a prediction model to obtain an output related to the input noise data (e.g., the output being a prediction related to the input noise data). One or more target output indications may be provided as reference feedback to the prediction model to update one or more portions of the prediction model, wherein the one or more portions of the prediction model are updated based on the related output and the target indications. Subsequent to the portions of the prediction model being updated, a data item may be provided to the prediction model to obtain a prediction related to the data item (e.g., a different version of the data item, a location of an aspect in the data item, etc.).

Abstract: An ablation catheter comprises an elongate catheter body and a branch electrode element. The elongate catheter body has a central axis, a distal end, and a proximal end. The elongate catheter body is configured to be steered within a heart chamber. The branch electrode element has a base end and a working end or tip. The base end is secured to the elongate catheter body at a location spaced proximally of the distal end and an effector on the working end. The branch electrode element is configured to evert when the distal end of the elongate catheter body is in a pulmonary vein ostium or os so that the effector can be selectively engaged against locations surrounding the pulmonary vein os which differ in radial direction and distance. One or more of linear or curved ablation patterns are generated on tissue adjacent the pulmonary vein os with the effector.

Abstract: In some embodiments, noise data may be used to train a neural network (or other prediction model). In some embodiments, input noise data may be obtained and provided to a prediction model to obtain an output related to the input noise data (e.g., the output being a prediction related to the input noise data). One or more target output indications may be provided as reference feedback to the prediction model to update one or more portions of the prediction model, wherein the one or more portions of the prediction model are updated based on the related output and the target indications. Subsequent to the portions of the prediction model being updated, a data item may be provided to the prediction model to obtain a prediction related to the data item (e.g., a different version of the data item, a location of an aspect in the data item, etc.).