公开(公告)号：US20250046057A1

公开(公告)日：2025-02-06

申请号：US18364101

申请日：2023-08-02

Applicant: GE Precision Healthcare LLC

Inventor： Rahul Venkataramani ,  Rachana Sathish ,  Krishna Seetharam Shriram ,  Chandan Kumar Mallappa Aladahalli ,  Christian Fritz Perrey ,  Michaela Hofbauer

IPC: G06V10/764 ,  G06T7/00 ,  G06T7/11 ,  G06V10/82

Abstract: A method for analyzing uncertainty in a multi-scale interpretation of a medical image includes inputting the medical image into a trained segmentation network. The method includes outputting via the trained segmentation network a segmentation output mask for each pixel of the medical image or a region of interest of the medical image. The method includes utilizing a deterministic function to aggregate segmentation output masks for all pixels of the medical image or the region of interest and to output a first classification prediction of the aggregated segmentation output masks. The method includes inputting the medical image into a trained classification network. The method includes outputting a second classification prediction of the medical image or the region of interest. The method includes determining an uncertainty between the first classification prediction and the second classification prediction via comparison of the first classification prediction to the second classification prediction.

公开(公告)号：US20250156709A1

公开(公告)日：2025-05-15

申请号：US18942209

申请日：2024-11-08

Applicant: GE Precision Healthcare LLC

Inventor： Rahul Venkataramani ,  Prasad Sudhakara Murthy ,  Rachana Sathish ,  KS Shriram

IPC: G06N3/08 ,  G06N3/045

Abstract: Methods and systems are provided for a customizable deep learning system. In one example, a system includes a processor and non-transitory memory storing instructions executable by the processor to receive a user selection of a time budget, enter an input to a deep learning system, the deep learning system including one or more deep learning models configured to generate a plurality of outputs based on the input, and wherein a number of outputs included in the plurality of outputs is based on the time budget, combine the plurality of outputs to form a final output, and output the final output for display on a display device, for downstream processing, and/or for storage in memory.

公开(公告)号：US20250149169A1

公开(公告)日：2025-05-08

申请号：US18504649

申请日：2023-11-08

Applicant: GE Precision Healthcare LLC

Inventor： Deepa Anand ,  Dattesh Shanbhag ,  Hariharan Ravishankar ,  Suresh Emmanuel Devadoss Joel ,  Rakesh Mullick ,  Rachana Sathish ,  Rahul Venkataramani ,  Krishna Seetharam Shriram ,  Prasad Sudhakara Murthy

IPC: G16H50/20 ,  G06T7/00 ,  G16H30/40

Abstract: Systems or techniques for facilitating learnable visual prompt engineering are provided. In various embodiments, a system can access a medical image and a pre-trained machine learning model that is configured to perform a diagnostic or prognostic inferencing task. In various aspects, the system can apply a pre-processing transformation to one or more pixels or voxels of the medical image, thereby yielding a transformed version of the medical image, wherein the pre-processing transformation can convert an input pixel or voxel intensity value to an output pixel or voxel intensity value via one or more parameters that are iteratively learned. In various instances, the system can perform the diagnostic or prognostic inferencing task, by executing the pre-trained machine learning model on the transformed version of the medical image.