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: For assertion detection from clinical text in a medical system, a model, such as a neural network, is trained to operate on multi-labeled clinical text. Using multi-task learning, both the scope and the class losses are minimized. As a result, a machine learning model can predict both the scope and class of clinical text for a patient where the clinical text is not limited to one class or a particular length.

Abstract: For viability determination with self-attention for process optimization, various process and state information in the manufacture (e.g., forming, assembling, and/or handling) of a part are embedded. A machine-learned model generates the embedding, which is used with self-attention similarity to identify similar cases based on the embedding. The model was trained using both regression for continuous information (e.g., variable names) in the embedding and classification for non-continuous information (e.g., value of a variable) in the embedding. By including both regression and classification, the same machine-learned model may be used for reliable and nuanced viability determination.

Abstract: Machine training is used to learn to generate findings in radiology reports. Rather than merely learning to output findings from an input, the machine training uses loss based on impression derived from findings to machine train the model to generate the findings. Once trained, the machine-learned model generates findings but the findings are more accurate or complete due to having used impression loss in the training.

Abstract: A computer-implemented method for analyzing log files generated by complex physical equipment includes receiving one or more log file generated by one or more components of physical equipment. Each of the log files comprises one or more log entries. A plurality of templates are extracted from each log file describing fixed portions of the log entries. The log entries are grouped in log files into a plurality of instances. Each instance corresponds to one of a plurality of partitions along one or more dimensions describing data in the log entries. A representation of each instance is created that describes a set of the templates included in the instance. A plurality of clusters are generated by applying a clustering process to the representations of the instances. A visual depiction of the clusters and the instances may then be created in a graphical user interface (GUI).

Abstract: Systems and methods for providing a means for improving the expressiveness and/or robustness of a machine learning system's result, based on imaging data and/or to make it possible to combine imaging data with non-imaging data to improve statements, which are deduced from the imaging data. The object is achieved by a computer implemented method, and uncertainty quantifier, medical system and a computer program product, and includes receiving a set of input data quantified as uncertainty, providing an information fusion algorithm, and applying the received set of input data on the provided information fusion algorithm, while modeling the propagation of uncertainty through the information fusion algorithm to predict an uncertainty for the medical assessment as a result (r), provided by the machine-learning system (M), based on the provided set of input data.

Abstract: Systems and methods for using a differentiable multi-agent Actor-Critic (DiMAC) for multi-step radiology report summarization. The tasks of extracting salient sentences and phrases are divided across two collaborating agents that are trained end-to-end using reinforcement learning (RL).

Abstract: A framework for generating reasons for imaging studies. An extractor, including a reinforcement learning agent, is trained to select one or more relevant sentences from the training histories of present illness. An abstractor is further pre-trained to generate one or more reasons for study from the one or more relevant sentences. An entity linking system is pre-trained using medical text corpora to map one or more mentions in the one or more reasons for study to one or more standardized medical entities for predicting one or more diagnoses. The reinforcement learning agent may then be re-trained using one or more rewards generated by the entity linking system. One or more reasons for study may be generated from a current history of present illness using the trained extractor, abstractor and entity linking system.

Abstract: For assertion detection from clinical text in a medical system, a model, such as a neural network, is trained to operate on multi-labeled clinical text. Using multi-task learning, both the scope and the class losses are minimized. As a result, a machine learning model can predict both the scope and class of clinical text for a patient where the clinical text is not limited to one class or a particular length.