Abstract: Brain tumor or other tissue classification and/or segmentation is provided based on from multi-parametric MRI. MRI spectroscopy, such as in combination with structural and/or diffusion MRI measurements, are used to classify. A machine-learned model or classifier distinguishes between the types of tissue in response to input of the multi-parametric MRI. To deal with limited training data for tumors, a patch-based system may be used. To better assist physicians in interpreting results, a confidence map may be generated using the machine-learned classifier.

Abstract: For decision support in a medical therapy, machine learning provides a machine-learned generator for generating a prediction of outcome for therapy personalized to a patient. The outcome prediction may be used to determine dose. To assist in decision support, a regression analysis of the cohort used for machine training relates the outcome from the machine-learned generator to the dose and an actual control time (e.g., time-to-event). The dose that minimizes side effects while minimizing risk of failure to a time for any given patient is determined from the outcome for that patient and a calibration from the regression analysis.

Abstract: By way of introduction, the present embodiments described below include apparatuses and methods for generating natural language representations of mental content from functional brain images. Given functional imaging data acquired while a subject reads a text passage, a reconstruction of the text passage is produced. Linguistic semantic vector representations are assigned (1301) to words, phrases or sentences to be used as training stimuli. Basis learning is performed (1305), using brain imaging data acquired (1303) when a subject is exposed to the training stimuli and the corresponding semantic vectors for training stimuli, to learn an image basis directly. Semantic vector decoding (1309) is performed with functional brain imaging data for test stimuli and using the image basis to generate a semantic vector representing the test imaging stimuli. Text generation (1311) is then performed using the decoded semantic vector representing the test imaging stimuli.

Abstract: For decision support in a medical therapy, machine learning provides a machine-learned generator for generating a prediction of outcome for therapy personalized to a patient. Deep learning may result in features more predictive of outcome than handcrafted features. More comprehensive learning may be provided by using multi-task learning where one of the tasks (e.g., segmentation, non-image data, and/or feature extraction) is unsupervised and/or draws on a greater number of training samples than available for outcome prediction alone.

Abstract: A computer-implemented method of monitoring dynamics of patient brain state during neurosurgical procedures includes receiving a plurality of brain response images acquired using a functional MRI scan of a patient over a plurality of time points and selecting a plurality of image features from the brain response images. One or more recurrent neural network (RNN) model are used to directly estimate one or more brain state measurements at each time point based on the image features. Once estimated, the brain state measurements are presented on a display.

Abstract: A computer-implemented method for decoding patient characteristics and brain state from multi-modality brain imaging data includes receiving a plurality of brain imaging datasets comprising brain imaging data corresponding to plurality of subjects. The brain imaging datasets are aligned to a common reference space and quantitative measures are extracted from each brain imaging dataset. Non-imaging characteristics corresponding to each subject are received and a forward model is trained to map the plurality of characteristics to the quantitative measures.

Abstract: A computer-implemented method for decoding brain imaging data of individual subjects by using additional imaging data from other subjects includes receiving a plurality of functional Magnetic Resonance Imaging (fMRI) datasets corresponding to a plurality of subjects. Each fMRI dataset corresponds to a distinct subject and comprises brain activation patterns resulting from presentation of a plurality of stimuli to the distinct subject. A group dimensionality reduction (GDR) technique is applied to the example fMRI datasets to yield a low-dimensional space of response variables shared by the plurality of subjects. A model is trained to predict a set of target variables based on the low-dimensional space of response variables shared by all subjects, wherein the set of target variables comprise one or more characteristics of the plurality of stimuli.

Abstract: An intelligent medical imaging scanner system includes an image scanner, an operator interface, a database, processors, and a storage medium. The database includes a learning model for relating configurations of the image scanner to operator input requirements. The storage medium contains programming instructions that, when executed, cause the processors to determine whether the learning model may be used to generate a configuration of the image scanner corresponding to the new input requirements. If the configuration can be generated, the processors use that configuration to acquire images of a patient using the image scanner. If the configuration of the image scanner cannot be generated, the processors perform an accommodation process comprising (a) modifying the learning model to generate a new configuration of the image scanner corresponding to the new input requirements, and (b) using the new configuration to acquire the images of the patient using the image scanner.

Abstract: An intelligent medical imaging scanner system includes an image scanner, an operator interface, a database, processors, and a storage medium. The database includes a learning model for relating configurations of the image scanner to operator input requirements. The storage medium contains programming instructions that, when executed, cause the processors to determine whether the learning model may be used to generate a configuration of the image scanner corresponding to the new input requirements. If the configuration can be generated, the processors use that configuration to acquire images of a patient using the image scanner. If the configuration of the image scanner cannot be generated, the processors perform an accommodation process comprising (a) modifying the learning model to generate a new configuration of the image scanner corresponding to the new input requirements, and (b) using the new configuration to acquire the images of the patient using the image scanner.

Abstract: An agricultural harvesting machine a chassis, a cabin and at least one mount coupling the cabin to the chassis. The mount has a first connecting feature connected to the cabin, a second connecting feature connected to the chassis and a resilient member coupled to both the first connecting feature and the second connecting feature. The resilient member has a shaped void therein.

Abstract: A combine side-shaking control system and a method for controlling operation of a side-shaking mechanism in a combine. The control system includes at least one sieve for separating crop material from other materials and a movable side-shaking mechanism coupled to the at least one sieve and configured to move the at least one sieve in a side-to-side motion. The control system also includes at least one sensor for sensing at least one operating condition of a combine system. The control system further includes a controller for receiving the at least one operating condition and causing the side-shaking mechanism to stop moving the at least one sieve in the side-to-side motion or start moving the at least one sieve in the side-to-side motion based on the at least one operating condition.

Abstract: An agricultural harvesting machine a chassis, a cabin and at least one mount coupling the cabin to the chassis. The mount has a first connecting feature connected to the cabin, a second connecting feature connected to the chassis and a resilient member coupled to both the first connecting feature and the second connecting feature. The resilient member has a shaped void therein.

Abstract: A combine side-shaking control system and a method for controlling operation of a side-shaking mechanism in a combine. The control system includes at least one sieve for separating crop material from other materials and a movable side-shaking mechanism coupled to the at least one sieve and configured to move the at least one sieve in a side-to-side motion. The control system also includes at least one sensor for sensing at least one operating condition of a combine system. The control system further includes a controller for receiving the at least one operating condition and causing the side-shaking mechanism to stop moving the at least one sieve in the side-to-side motion or start moving the at least one sieve in the side-to-side motion based on the at least one operating condition.

Abstract: A combine side-shaking control system and a method for controlling operation of a side-shaking mechanism in a combine. The control system includes at least one sieve for separating crop material from other materials and a movable side-shaking mechanism coupled to the at least one sieve and configured to move the at least one sieve in a side-to-side motion. The control system also includes at least one sensor for sensing at least one operating condition of a combine system. The control system further includes a controller for receiving the at least one operating condition and causing the side-shaking mechanism to stop moving the at least one sieve in the side-to-side motion or start moving the at least one sieve in the side-to-side motion based on the at least one operating condition.

Abstract: A combine side-shaking control system and a method for controlling operation of a side-shaking mechanism in a combine. The control system includes at least one sieve for separating crop material from other materials and a movable side-shaking mechanism coupled to the at least one sieve and configured to move the at least one sieve in a side-to-side motion. The control system also includes at least one sensor for sensing at least one operating condition of a combine system. The control system further includes a controller for receiving the at least one operating condition and causing the side-shaking mechanism to stop moving the at least one sieve in the side-to-side motion or start moving the at least one sieve in the side-to-side motion based on the at least one operating condition.

Abstract: A vibration isolator including at least one bearing element or assembly providing a contact interface along which structural elements connected together by the vibration isolator are allowed to substantially freely move within a limited range one relative to the other, with the effect of isolating the respective structural elements from vibrations or vibratory movements of the other structurally element, particular those vibratory movements directed or oriented at least generally along or parallel to the contact interface, with particular effect for isolating low frequency vibratory movements.

Abstract: A vibration isolator including at least one bearing element or assembly providing a contact interface along which structural elements connected together by the vibration isolator are allowed to substantially freely move within a limited range one relative to the other, with the effect of isolating the respective structural elements from vibrations or vibratory movements of the other structurally element, particular those vibratory movements directed or oriented at least generally along or parallel to the contact interface, with particular effect for isolating low frequency vibratory movements.

Abstract: A drive for transferring sideward motion from a first or driving elongate sickle knife to a second or driven elongate sickle knife longitudinally aligned with the first sickle knife. A double rack and pinion arrangement is used including at least one pinion mounted at a fixed location, a first sidewardly extending rack is connected to the first sickle knife for sideward movement therewith and a second sidewardly. extending rack is connected to the second sickle knife for sideward movement therewith, the racks both being enmeshed with the pinion such that sideward movement of the driving knife causes corresponding opposite sideward movement of the longitudinally aligned driven knife.

Abstract: A drive for transferring sideward motion from a first or driving elongate sickle knife to a second or driven elongate sickle knife longitudinally aligned with the first sickle knife. A double rack and pinion arrangement is used including at least one pinion mounted at a fixed location, a first sidewardly extending rack is connected to the first sickle knife for sideward movement therewith and a second sidewardly extending rack is connected to the second sickle knife for sideward movement therewith, the racks both being enmeshed with the pinion such that sideward movement of the driving knife causes corresponding opposite sideward movement of the longitudinally aligned driven knife.