Abstract: Systems and methods for performing a quality assessment of a medical imaging analysis task are provided. At least one low-field MRI (magnetic resonance imaging) quality assurance imaging data of the patient is received. A quality assessment of a medical imaging analysis task is performed based on the at least one low-field MRI quality assurance imaging data using one or more machine learning based networks. Results of the quality assessment are output.

Abstract: Systems and methods are provided for optimizing a deep learning model. A multi-site dataset associated with different clinical sites and a deployment dataset associated with a deployment clinical site are received. A deep learning model is trained based on the multi-site dataset. The trained deep learning model is optimized based on the deployment dataset. The optimized trained deep learning model is output.

Abstract: Systems and methods for performing a medical imaging analysis task using a machine learning based model are provided. One or more input medical images acquired using one or more out-of-distribution image acquisition parameters and having out-of-distribution imaging properties are received. The one or more out-of-distribution image acquisition parameters and the out-of-distribution imaging properties are out-of-distribution with respect to training data on which the machine learning based model is trained. One or more synthesized medical images are generated from the one or more input medical images using a machine learning based generator network. The one or more synthesized medical images are generated for one or more in-distribution image acquisition parameters and have in-distribution imaging properties.

Abstract: For data analytics in magnetic resonance (MR) scanning, the scanning configuration information and the resulting raw data are directly used to determine the analytics or clinical decision. Artificial intelligence provides a value for a clinical finding characteristic of the patient based on the raw data from scanning and the controls used to scan, allowing the value to be based on all of the information content of the scan results. Reconstruction is not needed, allowing for simpler hardware, such as hardware with less homogeneous B0 and/or B1 fields than the norm and/or non-linear gradients.

Abstract: For prediction of response of radiation therapy, radiomics are used for unsupervised machine training of an encoder-decoder network to predict based on input of image data, such as computed tomography image data and from segmentation. The trained encoder is then used to generate latent representations to be used as input to different classifiers or regressors for prediction of therapy responses, such as one classifier to predict response for an organ at risk and another classifier to predict another type of response for the organ at risk or to predict a response for the tumor.

Abstract: Systems and methods for predicting clinical outcomes of a patient are provided. An input medical image of a tumor of a patient is received. The tumor is segmented from the input medical image. One or more assessments of the tumor are performed based on the segmentation. A clinical outcome of the patient is predicted based on results of the one or more assessments of the tumor. The clinical outcome of the patient is output.

Abstract: Systems and methods are provided for optimizing a deep learning model. A multi-site dataset associated with different clinical sites and a deployment dataset associated with a deployment clinical site are received. A deep learning model is trained based on the multi-site dataset. The trained deep learning model is optimized based on the deployment dataset. The optimized trained deep learning model is output.

Abstract: Provided herein are methods and systems for planning radiation therapy. In examples, at least one processor can be programmed to: receive data associated with a first planning target volume and a first radiation map, provide the first planning target volume and the first radiation map to a first model to cause the first model to output data associated with at least one first beam position and least one first beam strength, generate a second radiation map, and provide the first planning target volume and the second radiation map to a second model to cause the second model to output data associated with at least one second beam position and least one second beam strength. At least one processor can be further programmed to: transmit data associated with the at least one second beam position and the least one second beam strength to cause a linear accelerator to deliver radiation.

Abstract: For uncertainty estimation for a machine-learned model prediction in medical imaging, a distribution in latent space is sampled to determine uncertainty in machine-learned model prediction. For example, a segmentation is output by a machine-learned model in response to input of multi-parametric data. A latent space generated by the machine-learned model is used to estimate the uncertainty of the segmentation, such as a segmentation of a prostate lesion. The model of Kohl, et al., may be used as the variational auto encoder generates a latent space representing the distribution of the training data, which latent space may be used to determine uncertainty.

Abstract: Systems and methods are provided for optimizing a deep learning model. A multi-site dataset associated with different clinical sites and a deployment dataset associated with a deployment clinical site are received. A deep learning model is trained based on the multi-site dataset. The trained deep learning model is optimized based on the deployment dataset. The optimized trained deep learning model is output.

Abstract: A neural network is trained using a training corpus having a plurality of information features, each of the information features including both a reference radiation treatment dose and at least one corresponding post-treatment patient datum. By one approach, the at least one corresponding post-treatment patient datum comprises patient imagery such as, but not limited to, one or more computed tomography images. That trained neural network facilitates radiation treatment planning by generating resultant treatment efficacy probability information and resultant treatment complications probability information.

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: Systems and method for performing a medical imaging analysis task via unsupervised domain adaptation are provided. 1) one or more input medical images of a patient and 2) one or more first image acquisition parameters associated with the one or more input medical images are received. One or more synthetic medical images associated with one or more second image acquisition parameters are generated. The one or more synthetic medical images are generated from at least one of the one or more input medical images using one or more machine learning based generator networks based on the one or more first image acquisition parameters. A medical imaging analysis task is performed using a machine learning based task network based on the one or more synthetic medical images. Results of the medical imaging analysis task are output.

Abstract: A harvester extends along a longitudinal direction, a lateral direction, and a vertical direction. The harvester includes a frame and a grain tank positioned above, and supported relative to, the frame. Moreover, the harvester includes an operator cabin extending between a forward side and an aft side and between an upper portion and a lower portion. The operator cabin is positioned above the frame and forward of the grain tank. Additionally, the harvester includes a coupling member coupling the operator cabin to the frame. The coupling member is mounted to the lower portion of the operator cabin. Furthermore, the harvester includes a damping member coupled between the grain tank and the upper portion of the operator cabin. The damping member is configured to limit motion of the coupling member relative to the frame during operations.

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: Systems and methods for determining whether input medical images are out-of-distribution of training images on which a machine learning based medical imaging analysis network is trained are provided. One or more input medical images of a patient are received. One or more reconstructed images of the one or more input medical images are generated using a machine learning based reconstruction network. It is determined whether the one or more input medical images are out-of-distribution from training images on which a machine learning based medical imaging analysis network is trained based on the one or more input medical images and the one or more reconstructed images. The determination of whether the one or more input medical images are out-of-distribution from the training images is output.

Abstract: Systems and methods for performing an assessment of one or more tumors are provided. A plurality of input medical images of a patient acquired at a plurality of points in time is received. One or more tumors are identified in each of the plurality of input medical images. A tumor burden of the patient is determined for each of the plurality of points in time based on the one or more identified tumors using one or more machine learning based networks. An assessment of the one or more tumors is performed based on the tumor burden of the patient determined for each of the plurality of points in time. Results of the assessment of the one or more tumors are output.

Abstract: In an method for training artificial intelligence entities (AIE) for abnormality detection, medical imaging data of the human organ is provided as training data having training samples, the medical imaging data including imaging results from different types of imaging techniques for each training sample of the training data, a pre-trained or randomly initialized AIE is provided, and the AIE is trained using the provided training samples. The training may include, for at least one training sample, a first loss function for a sub-structure of the AIE is calculated independently of a first spatial region of the human organ, and, for a training sample, a second loss function for a sub-structure of the AIE is calculated independently of a second spatial region of the human organ. The AIE may be trained using the calculated first loss function and the calculated second loss function.

Abstract: Systems and methods for performing a medical imaging analysis task using a machine learning based model are provided. One or more input medical images acquired using one or more out-of-distribution image acquisition parameters and having out-of-distribution imaging properties are received. The one or more out-of-distribution image acquisition parameters and the out-of-distribution imaging properties are out-of-distribution with respect to training data on which the machine learning based model is trained. One or more synthesized medical images are generated from the one or more input medical images using a machine learning based generator network. The one or more synthesized medical images are generated for one or more in-distribution image acquisition parameters and have in-distribution imaging properties.

Abstract: For autonomous MR scanning for a given medical test, a simplified MR scanner may be used without or will little input or control by a technologist (e.g., by a physician, radiologist, or person trained in MR scanner operation). The MR scanner autonomously positions, scans, checks quality, analyzes, and/or outputs an answer to a diagnostic question with or without an MR image. Scan analysis, based on artificial intelligence, allows for on-going or on-the-fly alteration of the scanning configuration to acquire the data desired to answer the diagnostic question. By using a simplified MR scanner, both position of the patient relative to the MR scanner and localization of the scan by the MR scanner are jointly solved. Sensors may sense a patient in a scan position where the reduced radio frequency requirements allow for a more open bore.