Abstract: Apparatuses, systems, and techniques to generate one or more neural networks. In at least one embodiment, one or more neural networks are generated, based on, for example, one or more convolutional neural network operations and one or more transformer neural network operations.

Abstract: Apparatuses, systems, and techniques are presented to predict annotations for objects in images. In at least one embodiment, boundaries of an object within an image can be identified based, at least in part, on a user-generated outline of only a portion of this object or information about a size of this object provided by a user.

Abstract: Apparatuses, systems, and techniques are presented to predict annotations for objects in images. In at least one embodiment, one or more neural networks are used to help generate one or more segmentation boundaries of one or more objects within one or more digital images, wherein the one or more neural networks are to transform one or more representations of one or more portions of the one or more objects into one or more lower-dimensional representations of the one or more portions of the one or more objects.

Abstract: Apparatuses, systems, and techniques to generate one or more images of an object. In at least one embodiment, a technique includes training one or more neural networks to generate one or more images of an object from at least a first image of the object and a second lower-resolution image of the object, where the training includes a comparison of the one or more generated images of the object with the second lower-resolution image of the object.

Abstract: Apparatuses, systems, and techniques to improve federated learning for neural networks. In at least one embodiment, a federated server dynamically selects neural network weights according to one or more learnable aggregation weights indicating a contribution from each of one or more edge devices or clients during federated training according to various characteristics of each edge device or client model and training data.

Abstract: Apparatuses, systems, and techniques to select a neural network using an amount of memory to be used. In at least one embodiment, a processor includes one or more circuits to cause one or more neural networks to be selected from a plurality of neural networks based, at least in part, on an amount of memory to be used by the one oe more neural networks.

Abstract: Apparatuses, systems, and techniques to facilitate global semi-supervised training of neural networks to perform image segmentation related to diagnosis and management of emerging diseases, such as COVID-19. In at least one embodiment, distributed client training frameworks train one or more client neural networks to perform image segmentation according to a local training data set as well as global neural network data aggregated, by one or more central servers, from each of one or more globally distributed client neural networks.

Abstract: Apparatuses, systems, and techniques to train one or more neural networks based, at least in part on, medical imaging data and clinical metadata or inference using one or more neural networks trained as such. In at least one embodiment, one or more circuits to train one or more neural network to predict a treatment for a patient suspected to have or confirmed to have COVID-19 based, at least in part on, medical imaging data and clinical metadata.

Abstract: Apparatuses, systems, and techniques to generate an optimized neural network architecture. In at least one embodiment, various neural network components are used to generate one or more neural network configurations, and each neural network configuration is trained in order to determine an optimal neural network architecture for a training dataset.

Abstract: Apparatuses, systems, and techniques to train one or more neural networks to generate labels for unsupervised or partially-supervised data. In at least one embodiment, one or more pseudolabels are generated by a training framework based on available weak annotations for an input medical image, and combined with feature information about said input medical image generated by one or more neural networks to generate a label about said input medical image.

Abstract: Apparatuses, systems, and techniques are described herein to speed up inferencing in a neural network by copying output from one layer of the neural network to another computing resource based on dependencies among layers in the network. In at least one embodiment, a processor comprising one or more circuits causes two or more subsequent layers of one or more neural networks to be performed on separate computing resources from a previous layer of the one or more neural networks.

Abstract: Apparatuses, systems, and techniques to select a nueral network architecture from a plurality of neural networs in a federated learning (FL) settng. In at least one embodiment, a neural network is trained by combining training resutls from different FL computing systesms, where each of the different FL computing systems, for example, trains different portions of the nerual network.

Abstract: In at least one embodiment, a reinforcement-learning-based searching approach is used to produce a training configuration for a machine-learning model. In at least one embodiment, 3D medical image segmentation is performed using learned image preprocessing parameters.

Abstract: In at least one embodiment, a reinforcement-learning-based searching approach is used to produce a training configuration for a machine-learning model. In at least one embodiment, 3D medical image segmentation is performed using learned image preprocessing parameters.

Abstract: Apparatuses, systems, and techniques to perform training of neural networks using stacked transformed images. In at least one embodiment, a neural network is trained on stacked transformed images and trained neural network is provided to be used for processing images from an unseen domain distinct from a source domain, wherein stacked transformed images are transformed according to transformation aspects related to domain variations.

Abstract: In various examples, a virtualized computing platform for advanced computing operations—including image reconstruction, segmentation, processing, analysis, visualization, and deep learning—may be provided. The platform may allow for inference pipeline customization by selecting, organizing, and adapting constructs of task containers for local, on-premises implementation. Within the task containers, machine learning models generated off-premises may be leveraged and updated for location specific implementation to perform image processing operations. As a result, and using the virtualized computing platform, facilities such as hospitals and clinics may more seamlessly train, deploy, and integrate machine learning models within a production environment for providing informative and actionable medical information to practitioners.