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: Image-based biomarkers are provided for active disease progression of Alzheimer's disease and related dementias (ADRD) by training and using artificial neural networks (ANNs) on the basis of subject's images. A cross-sectional sub-pipeline and a longitudinal sub-pipeline are used for processing different images parts, namely cross-sectional imaging data and longitudinal imaging data. A patch-based multiple instance learning (MIL) scheme is applied.

Abstract: Systems and methods for performing a medical imaging analysis task using a foundation model are provided. One or more 3D (three-dimensional) medical images each comprising a plurality of 2D (two-dimensional) slices are received. A first set of features is extracted from the plurality of 2D slices of the one or more 3D medical images using a machine learning based encoding network. Each respective feature of the first set of features is resampled based on a spatial location of one or more pixels of the 2D slices from which the respective feature was extracted. The resampled first set of features is encoded into a second set of features. A medical imaging analysis task is performed based on the second set of features. Results of the medical imaging analysis task are output.

Abstract: Systems and methods for performing a medical imaging analysis task conditioned on multi-domain medical images with missing modalities are provided. 1) one or more medical images each in a different domain and 2) a domain code defining a presence of the different domains in a set of predefined domains are received. One or more weights are determined based on the domain code. One or more parameters of a machine learning based encoder are updated based on the one or more weights. Features are extracted from the one or more medical images using the machine learning based encoder with the one or more updated parameters. A medical imaging analysis task is performed based on the extracted features. Results of the medical imaging analysis task are output.

Abstract: Systems and methods for performing a medical imaging analysis task using a universal foundation model are provided. 1) one or more input medical images each in a domain and 2) a domain code for each of the one or more input medical images identifying its domain are provided. For each respective one of the domain codes, one or more weights are determined based on the respective domain code. One or more parameters of a dynamic convolutional layer are updated based on the one or more weights. A first set of features is extracted from the one or more input medical images using the dynamic convolutional layer with the one or more updated parameters. The first set of features are encoded into a second set of features using a machine learning based encoder. A medical imaging analysis task is performed based on the second set of features. Results of the medical imaging analysis task are output.

Abstract: An ultrasound scanner scans a patient. The scan data is checked for an optimal view as a subpart of the scan. Where the field of view does not include the optimal view, the scan is repeated with a different spatial extent. Where the field of view does include the optimal view, then the scan data for that optimal view is used to generate an image. Both the view and the overall or broader scan are controlled together so that a sonographer may place the transducer at an approximate location to still provide a precise view.

Abstract: Systems and methods for performing a medical imaging analysis task based on pixelwise positionally encoded features are provided. One or more input medical images are received. One or more pixelwise positional embedding images are generated for the one or more input medical images using a spatially varying function. Patches are extracted from the one or more input medical images and the one or more pixelwise positional embedding images. The patches extracted from the one or more input medical images are encoded with corresponding ones of the patches extracted from the one or more pixelwise positional embedding images into pixelwise positionally encoded features. A medical imaging analysis task is performed using a machine learning based network based on the pixelwise positionally encoded features. Results of the medical imaging analysis task are output.

Abstract: Systems and methods for generating a patient-specific vascular tree are provided. 1) one or more medical images of a patient and 2) a vascular tree template comprising a plurality of points are received. The plurality of points of the vascular tree template are iteratively adjusted based on the one or more medical images using one or more AI (artificial intelligence) agents to generate a patient-specific vascular tree for the patient. The patient-specific vascular tree is output.

Abstract: Computer-implemented methods and systems for providing an image acquisition information of a medical image are provided. A method includes obtaining the medical image; extracting image data from the medical image; obtaining non-image data associated with the image data of the medical image; providing a first trained function configured to determine an image acquisition information based on the non-image data; providing a second trained function configured to determine an image acquisition information based on the image data of the medical image; determining a first image acquisition information by applying the first trained function to the non-image data; determining a second image acquisition information by applying the second trained function to the image data; determining the image acquisition information based on the first image acquisition information and the second image acquisition information; and providing the image acquisition information.

Abstract: Systems and methods for generating synthetic images representing healthy-for-age images of an anatomical object are provided. 1) one or more input medical images of an anatomical object of a patient and 2) an input age associated with the patient are received. A feature set is extracted from the one or more input medical images. The extracted feature set is encoded with noise based on the input age associated with the patient using a machine learning based noise model. An age associated with the patient is predicted based on the extracted feature set. The encoded feature set is denoised based on the input age associated with the patient and the predicted age associated with the patient using a machine learning based denoising model. One or more synthetic images of the anatomical object of the patient are generated based on the denoised feature set. The one or more synthetic images of the anatomical object of the patient are output.

Abstract: A computer-implemented training data preparation method comprises: receiving an input medical image of vessels of a patient; determining a vessel segmentation from the input medical image; identifying and annotating anatomical landmarks in the vessel segmentation to produce an annotated vessel segmentation; and storing the annotated vessel segmentation as training data. A training method for training neural networks based on the training data and a medical diagnostic method applying trained AI models are also provided.

Abstract: Systems and methods for performing a medical imaging analysis task are provided. 1) a plurality of medical images acquired at a plurality of acquisition orientations and in one or more domains and 2) a domain code for each particular acquisition orientation of the plurality of acquisition orientations are received. Each of the domain codes identify a presence of the one or more domains of the plurality of medical images that were acquired at the particular acquisition orientation. For each of the particular acquisition orientations, the domain code for the particular acquisition orientation are encoded, features are extracted from the plurality of medical images that were acquired at the particular acquisition orientation, and the encoded domain code and the extracted features are combined to generate image features for the particular acquisition orientation. A medical imaging analysis task is performed based on the image features for each of the particular acquisition orientations.

Abstract: Systems and methods for generating augmented images are provided. One or more input medical images are received. At least one of noise and one or more transformations are applied to the one or more input medical images to generate one or more noisy augmented images. The one or more noisy augmented images are denoised using a diffusion-based denoising system to generate one or more denoised augmented images. The applying and the denoising are repeated for one or more iterations using the one or more denoised augmented images as the one or more input medical images to generate one or more final augmented images. The one or more final augmented images are output.

Abstract: Systems and methods for generating a guided review of the one or more input medical images are provided. One or more input medical images of a patient and text-based patient data of the patient are received. One or more clinical tasks are identified based on the text-based patient data using a language model. One or more machine learning based models are selected based on the one or more identified clinical tasks. One or more medical imaging analysis tasks are performed based on the one or more input medical images using the one or more selected machine learning based models. A guided review of the one or more input medical images is generated based on results of the one or more medical imaging analysis tasks. The guided review of the one or more input medical images is output.

Abstract: Systems and methods for generating synthesized medical images of a tumor are provided. A 3D mask of an anatomical structure generated from a 3D medical image and a 3D image of a plurality of concentric spheres are received. A 3D mask of a tumor is generated based on the 3D mask of the anatomical structure and the 3D image of the plurality of concentric spheres using a first 3D generator network. A 3D intensity map of the tumor is generated based on the 3D mask of the tumor and the 3D image of the plurality of concentric spheres using a second 3D generator network. A 3D synthesized medical image of the tumor is generated based on one or more 2D slices of the 3D intensity map of the tumor and one or more 2D slices of the 3D medical image using a 2D generator network. The 3D synthesized medical image of the tumor is output.

Abstract: A scheduling system includes: a plurality of input devices configured to output medical data, a workforce storage, configured to store working characteristics of a plurality of doctors, and a scheduler configured to receive as input data related to the medical data and the working characteristics, and configured to provide as output a plurality of schedules for the plurality of doctors for analysing the medical data.

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: Systems and methods for performing a medical imaging analysis task based on pixelwise positionally encoded features are provided. One or more input medical images are received. One or more pixelwise positional embedding images are generated for the one or more input medical images using a spatially varying function. Patches are extracted from the one or more input medical images and the one or more pixelwise positional embedding images. The patches extracted from the one or more input medical images are encoded with corresponding ones of the patches extracted from the one or more pixelwise positional embedding images into pixelwise positionally encoded features. A medical imaging analysis task is performed using a machine learning based network based on the pixelwise positionally encoded features. Results of the medical imaging analysis task are output.

Abstract: Systems and methods for segmenting one or more lesions from medical image patches are provided. An input medical image patch depicting one or more lesions is received. The one or more lesions are segmented from the input medical image patch using a plurality of machine learning based segmentation networks to respectively generate a plurality of initial segmentation masks. Each of the plurality of machine learning based segmentation networks is trained to segment lesions from patches with a different field of view size. A final segmentation mask of the one or more lesions is generated based on the plurality of initial segmentation masks. The final segmentation mask of the one or more lesions is output.

Abstract: Systems and methods for longitudinal change analysis are provided. A first medical image depicting an anatomical object at a first time and a second medical image depicting the anatomical object at a second time are received. The first medical image is encoded into a first set of features and the second medical image is encoded into a second set of features. The first set of features and the second set of features are encoded into a set of longitudinal features. A medical imaging analysis task is performed on longitudinal changes depicted in the first medical image and the second medical image using a machine learning based network based on the set of longitudinal features. Results of the medical imaging analysis task are output.