公开(公告)号：US20250166194A1

公开(公告)日：2025-05-22

申请号：US18951587

申请日：2024-11-18

Applicant: GE Precision Healthcare LLC

Inventor： Hongxu Yang ,  Xiaomeng Dong ,  Pál Tegzes ,  Lehel Mihály Ferenczi ,  Gopal Biligeri Avinash ,  Yunfeng Li ,  Michail Fanariotis

IPC: G06T7/00 ,  G06T5/60 ,  G06V10/82

Abstract: A technique to optimize medical image enhancement that is facilitated by AI/deep learning neural network implementation. In various embodiments, the computer-executable components can comprise a receiving component that receives a set of “regions/volume of interest” images containing a plurality of organs; and an artificial intelligence deep learning neural network model component that automatically processes and enhances the respective images in a locally adaptive way so that at each location the enhanced image is optimized for the organ that is displayed at that location.

公开(公告)号：US12249067B2

公开(公告)日：2025-03-11

申请号：US17664702

申请日：2022-05-24

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Hongxiang Yi ,  Rakesh Mullick ,  Lehel Mihály Ferenczi ,  Gopal Biligeri Avinash ,  Borbála Deák-Karancsi ,  Balázs Péter Cziria ,  Laszlo Rusko

IPC: G06T7/00 ,  A61B8/08 ,  G06T7/149 ,  G06T7/174

Abstract: Techniques are described that facilitate dynamic multimodal segmentation selection and fusion in medical imaging. In one example embodiment, a computer processing system receives a segmentation dataset comprising a combination of different image segmentations of an anatomical object of interest respectively segmented via different segmentation models from different medical images captured of the (same) anatomical object, wherein the different medical images and the different image segmentations vary with respect to at least one of, capture modality, acquisition protocol, or acquisition parameters. The system employs a dynamic ranking protocol as opposed to a static ranking protocol to determine ranking scores for the different image segmentations that control relative contributions of the different image segmentations in association with combining the different image segmentations into a fused segmentation for the anatomical object. The system further combines the different image segmentations based on the ranking scores to generate the fused image segmentation.

公开(公告)号：US20240127047A1

公开(公告)日：2024-04-18

申请号：US18046347

申请日：2022-10-13

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Hongxu Yang ,  Gopal Biligeri Avinash ,  Balázs Péter Cziria ,  Pál Tegzes ,  Xiaomeng Dong ,  Ravi Soni ,  Lehel Mihály Ferenczi ,  Laszlo Rusko

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/0454

Abstract: Systems/techniques that facilitate deep learning image analysis with increased modularity and reduced footprint are provided. In various embodiments, a system can access medical imaging data. In various aspects, the system can perform, via execution of a deep learning neural network, a plurality of inferencing tasks on the medical imaging data. In various instances, the deep learning neural network can comprise a common backbone in parallel with a plurality of task-specific backbones. In various cases, the plurality of task-specific backbones can respectively correspond to the plurality of inferencing tasks.

公开(公告)号：US20230162352A1

公开(公告)日：2023-05-25

申请号：US17951281

申请日：2022-09-23

Applicant: GE Precision Healthcare LLC

Inventor： Pal Tegzes ,  Zita Herczeg ,  Tao Tan ,  Balazs Peter Cziria ,  Alec Joseph Baenen ,  Gireesha Chintharmani Rao ,  Lehel Ferenczi ,  Gopal Biligeri Avinash ,  Zoltan Kiss ,  Hongxu Yang ,  Beth Ann Heckel

IPC: G06T7/00 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G16H50/20 ,  G06T2207/20081

Abstract: An image processing system is provided. The image processing system includes a display, a processor, and a memory. The memory stores processor-executable code that when executed by the processor causes receiving an image of a region of interest of a patient with an enteric tube or line disposed within the region of interest, detecting the medical tube or line within the image, generating a combined image by superimposing graphical markers on the image that indicate placement or misplacement of the enteric tube or line, and displaying the combined image on a display. In further aspects, a classification of the enteric tube or line (e.g., correctly placed tube present, malpositioned tube present, and so forth) may be determined and communicated to one or more clinicians. Additionally, the outputs of the image processing system may also be provided to facilitate triage of patients, helping prioritize which tube placements require further attention and in what order.

公开(公告)号：US11537885B2

公开(公告)日：2022-12-27

申请号：US16773156

申请日：2020-01-27

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Min Zhang ,  Gopal Biligeri Avinash ,  Lehel Ferenczi ,  Levente Imre Török ,  Pál Tegzes

IPC: G06N3/08 ,  G06N20/00 ,  G06N3/04

Abstract: Systems and techniques that facilitate freeze-out as a regularizer in training neural networks are presented. A system can include a memory and a processor that executes computer executable components. The computer executable components can include: an assessment component that identifies units of a neural network, a selection component that selects a subset of units of the neural network, and a freeze-out component that freezes the selected subset of units of the neural network so that weights of output connections from the frozen subset of units will not be updated for a training run.