Abstract: Systems and methods for performing a medical imaging analysis task using a machine learning based model (e.g., a foundational model) are provided. One or more input medical images are received. One or more modality-specific expert subnetworks of the machine learning based model are selected for performing a first processing of the one or more input medical images. The first processing of the one or more input medical images is performed for performing a medical imaging analysis task using the one or more selected modality-specific expert subnetworks. Results of the first processing are merged into one or more sets of merged results. One or more cross-modality expert subnetworks of the machine learning based model are selected for performing a second processing of the one or more sets of merged results. The second processing of the one or more sets of merged results is performed for performing the medical imaging analysis task using the one or more selected cross-modality expert subnetworks.

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 performing one or more medical imaging analysis tasks are provided. A sequence of medical images is received. One or more patches are extracted from each image of the sequence of medical images. Spatio-temporal features are extracted from the one or more extracted patches using a machine learning based encoder network. One or more medical imaging analysis tasks are performed based on the extracted spatio-temporal features. Results of the one or more medical imaging analysis tasks are output. The machine learning based encoder network is trained by receiving a sequence of training medical images. Patches of a first set of images of the sequence of training medical images are masked according to a first masking strategy. Patches of a second set of images of the sequence of training medical images are masked according to a second masking strategy.

Abstract: A position prediction of a target is provided based on a segmentation of a context of the target. Alternatively, or additionally, to a spatial context of the target, it is also possible to consider a temporal context. A catheter tip can be tracked.

Abstract: A vessel imaging sequence including plurality of vessel imaging frames and a corresponding ECG signal are generated by encoding, using a first encoder, an input vessel imaging sequence to generate a plurality of vessel latent space vectors, each vessel latent space vector corresponding to an input vessel imaging frame of the input vessel imaging sequence, encoding, using a second encoder, an input ECG signal to generate a plurality of ECG latent space vectors, decoding, using a first decoder, the plurality of vessel latent space vectors and the plurality of ECG latent space vectors to generate the vessel imaging sequence and decoding, using a second decoder, the vessel latent space vector and the ECG latent space vector to generate the ECG signal.

Abstract: A tip of a vascular catheter is detected in a sequence of vessel image frames obtained using x-rays. Within the vessel image frames, a proximal point corresponding to a vessel ostium is determined. The sequence of vessel image frames are cropped to an image area surrounding the proximal point to generate a cropped sequence of cropped vessel image frames. Within cropped vessel image frames not indicative of a contrast medium, the tip of the vascular catheter is detected, and the detected tip of the vascular catheter is tracked.

Abstract: A computer-implemented pathological vessel guidance method includes the acts of generating a vessel roadmap library, the vessel roadmap library including a plurality of vessel roadmaps, wherein each vessel roadmap includes a vessel roadmap image and first and second alignment data; detecting, within the vessel roadmap images of the vessel roadmap library, a pathological vessel; obtaining a real-time fluoroscopy image and corresponding real-time first and second fluoroscopy information; selecting a vessel roadmap from the roadmap library based on comparing the first real-time fluoroscopy information with the first alignment data of each vessel roadmap of the roadmap library; overlaying the real-time fluoroscopy image with the selected vessel roadmap image of the vessel roadmap library; aligning the vessel roadmap image of the selected vessel roadmap and the real-time fluoroscopy image based on the second alignment data and the real time second fluoroscopy information; and providing guidance for a fluoroscopy o

Abstract: Systems and methods for performing a medical imaging analysis task are provided. One or more input medical images of a patient are received. The one or more input medical images are encoded into embeddings using a machine learning based encoder network. A medical imaging analysis task is performed based on the embeddings. Results of the medical imaging analysis task are output.

Abstract: The present application relates to a computer-implemented dynamic vessel roadmapping method. The method comprises generating a vessel roadmap library, the vessel roadmap library including a plurality of vessel roadmaps, wherein each vessel roadmap comprises vessel roadmap image and first and second alignment data, obtaining a real-time fluoroscopy image and corresponding real-time first and second fluoroscopy information, overlaying a vessel roadmap image of the vessel roadmap library with the real-time fluoroscopy image based on the first alignment data, the second alignment data, and the real time first and second fluoroscopy information and aligning the vessel roadmap image and the real-time fluoroscopy image based on the second alignment data and the real time second fluoroscopy information, wherein the second alignment data is derived from the corresponding vessel roadmap image.

Abstract: The present invention provides a system for vehicle security, personalization, and cardiac activity monitoring of a driver wherein electrocardiography of a driver is monitored and registered which is used for identification of a person entering in the vehicle and personalization of vehicle based on user preferences thereby act as intruder detection towards vehicle security. In addition to registration the present invention also monitors cardiac activity of driver in a continuous and real time fashion without any intrusion to driver with the facility of generation of alert and making emergency call.

Abstract: The present invention provides a system for vehicle security, personalization, and cardiac activity monitoring of a driver wherein electrocardiography of a driver is monitored and registered which is used for identification of a person entering in the vehicle and personalization of vehicle based on user preferences thereby act as intruder detection towards vehicle security. In addition to registration the present invention also monitors cardiac activity of driver in a continuous and real time fashion without any intrusion to driver with the facility of generation of alert and making emergency call.