Abstract: Systems and methods for performing a medical analysis task using a trained machine learning based task network are provided. Input medical data is received. A medical analysis task is performed using a trained machine learning based task network based on the input medical data. Results of the medical analysis task are output. The trained machine learning based task network is trained by: receiving unannotated training medical data; generating weakly-supervised labels for the unannotated training medical data using one or more trained machine learning based supervised learning networks; training the machine learning based task network for performing the medical analysis task based on 1) the unannotated training medical data, 2) self-supervised labels for the unannotated training medical data learned via self-supervised learning, and 3) the generated weakly-supervised labels for the unannotated training medical data; and outputting the trained machine learning based task network.

Abstract: Systems and methods for generating a response summarizing patient data are provided. One or more prompts, comprising 1) patient data retrieved from one or more patient databases and 2) instructions, are received. A response summarizing the patient data is generated based on the instruction using a large language model. The response is output.

Abstract: A system for synthesizing medical images including synthesizing medical abnormalities has multiple diffusion model based denoising stages. At a first denoising stage, a machine-learned network denoises a first noise input to obtain an abnormality spatial mask detailing positional and structural characteristics of the synthesized medical abnormality. At a second denoising stage, a machine-learned network denoises a second noise input based on the abnormality spatial mask and a pre-abnormality image to obtain a synthesized medical image that corresponds to the pre-abnormality image with the synthesized medical abnormality inserted consistent with the abnormality spatial mask.

Abstract: Systems and methods for determining an uncertainty measure associated with results of a medical task performed by an LLM (large language model) are provided. One or more prompts associated with a medical task are received. At least one of the one or more prompts are encoded into a set of features using a feature encoder network of an LLM. The medical task is performed based on the set of features using a decoder network of the LLM. An uncertainty measure associated with results of the medical task is determined based on the set of features using an uncertainty quantification module of the LLM. The results of the medical task and the uncertainty measure are output.

Abstract: Systems and methods for determining a segmentation of a hemorrhage are provided. An input medical image of a hemorrhage of a patient is received. A contour-sensitive segmentation of the hemorrhage from the input medical image is performed using a machine learning based contour-sensitive segmentation network. A detection-sensitive segmentation of the hemorrhage from the input medical image is performed using a machine learning based detection-sensitive segmentation network. A final segmentation of the hemorrhage from the input medical image is determined based on results of the contour-sensitive segmentation and results of the detection-sensitive segmentation. The final segmentation of the hemorrhage is output.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: A computer-implemented method for detecting a failure of a device connected to a sensor is disclosed. The method includes a machine learning model receiving a trace signal from the sensor indicating a status of the device, the machine learning model encoding the trace signal into a plurality of vector representations, and the machine learning model determining whether the trace signal is valid or invalid based on the plurality of vector representations.

Abstract: A computer-implemented method for pretraining a downstream neural network for a novel image-to-image task to be performed on medical imaging data received from a medical scanner is provided. A database of augmented training data sets is generated based on a database of pre-existing training data sets. A set of at least two pretext neural network subsystems are jointly trained for performing (in particular partly self-supervised and partly weakly supervised) pretext tasks using the generated database. The downstream neural network is pretrained for the novel image-to-image task to be performed on medical imaging data received from a medical scanner. The pretraining is based on a subset of the modified weights of the pretext neural network subsystems, and/or on an output of a subset of layers of the set of pretext neural network subsystems.

Abstract: Systems and methods for generating a probabilistic tree of vessels are provided. An input medical image of vessels of a patient is received. Anatomical landmarks are identified in the input medical image. A centerline of the vessels in the input medical image is determined based on the anatomical landmarks. A probabilistic tree of the vessels is generated based on a probability of fit of the anatomical landmarks and the centerline of the vessels. The probabilistic tree of the vessels is output.

Abstract: Systems and method for performing a medical imaging analysis task for making a clinical decision are provided. One or more input medical images of a patient are received. A medical imaging analysis task is performed from the one or more input medical images using a machine learning based network. The machine learning based network generates a probability score associated with the medical imaging analysis task. An uncertainty measure associated with the probability score is determined. A clinical decision is made based on the probability score and the uncertainty measure.

Abstract: Systems and methods for training an artificial intelligence-based system using self-supervised learning are provided. For each respective training medical image of a set of unannotated training medical images, the following steps are performed. A first augmented image is generated by applying a first augmentation operation to the respective training medical image. A second augmented image is generated by applying a second augmentation operation to the respective training medical image. A first representation vector is created from the first augmented image using an encoder network. A second representation vector is created from the second augmented image using the encoder network. The first representation vector is mapped to first cluster codes. The second representation vector is mapped to second cluster codes. The encoder network is optimized using the first and second representation vectors and the first and second cluster codes.

Abstract: For AI-based recommendations in a service management system, the AI is machine trained using gamification. A model of the service management system is used in simulation to train a policy in reinforcement learning to implement strategies for improvement of KPI(s). By varying sampling of distribution of parameters of the model and/or varying the distributions of parameters used in the model, the policy learns to deal with a variety of situations using the simulations from the model. The resulting AI (machine-learned policy) is used to make recommendations for the service management system.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: Systems and methods for quantification of body composition using contrastive learning in computed tomography (CT) data. A segmentation model is provided that is trained using two stages. An encoder of the segmentation model is pretrained using unlabeled data. The encoder is extended by a decoder which is further trained using labeled data.

Abstract: A 3D shape is reconstructed from a topogram. A generative network is machine trained. The generative network includes a topogram encoder for inputting the topogram and a decoder to output the 3D shape from the output of the encoder. For training, one or more other encoders are included, such as for input of a mask and/or input of a 3D shape as a regularlizer. The topogram encoder and decoder are trained with the other encoder or encoders outputting to the decoder. For application, the topogram encoder and decoder as trained, with or without the encoder for the mask and without the encoder for the 3D shape, are used to estimate the 3D shape for a patient from input of the topogram for that patient.

Abstract: In accordance with an embodiment, a method for characterizing a non-volatile memory, includes: applying a first voltage on a word line conductively coupled to a non-volatile memory cell and measuring a current flowing through the non-volatile memory cell in response to applying the first voltage. Measuring the current includes: using a sense amplifier, comparing the current flowing through the non-volatile memory cell with a plurality of different first currents generated by an adjustable current source while applying the same first voltage on the word line, and determining the measured current based on the comparing.

Abstract: Provided are computer-implemented methods and systems for classifying a medical image data set. In particular, a method is provided comprising the steps of receiving the medical image dataset of a patient, of providing a first classification stage configured to classify the medical image dataset as normal or not-normal, of providing a second classification stage different than the second classification stage and configured to classify the medical image dataset as normal or not-normal, and of subjecting the medical image dataset to the first classification stage to classify the medical image dataset as normal or not-normal. Further, the method comprises subjecting the medical image dataset to the second classification stage to classify the medical image dataset as normal or not-normal, if the medical image dataset is classified as normal in the first classification stage.

Abstract: Systems and methods for performing a medical imaging analysis task are provided. A plurality of 3D (three dimensional) patches extracted from a 3D input medical image is received. A set of local features is extracted from each of the plurality of 3D patches using a machine learning based local feature extractor network. Global features representing relationships between the sets of local features are determined. A medical imaging analysis task is performed on the 3D input medical image based on the global features. Results of the medical imaging analysis task are output.

Abstract: Systems and methods for occlusion detection in medical images are provided. An input medical image of one or more vessels in an anatomical object of a patient is received. One or more anatomical landmarks are identified in the input medical image. A first patch and one or more additional patches are extracted from the input medical image based on the identified one or more anatomical landmarks. The first patch and the one or more additional patches depict different portions of the anatomical object. Features are extracted from the first patch and the one or more additional patches using a machine learning based feature extractor network. An occlusion in the one or more vessels is detected in the first patch based on the extracted features with or without modeling features on a probability distribution function. Results of the detecting are output.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.