公开(公告)号：US20250014721A1

公开(公告)日：2025-01-09

申请号：US18761131

申请日：2024-07-01

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： ZiYu Fan ,  Jianming Liang

IPC: G16H30/40 ,  G06T5/40 ,  G06T5/92 ,  G06V10/26 ,  G06V10/32 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82

Abstract: A generic unified deep model for learning from multiple tasks, in the context of medical image analysis includes means for receiving a training dataset of medical images; training the AI model to generate a trained AI model using a pre-processing operation, a Swin Transformer-based segmentation operation, and a post-processing operation, in which application of a Non-Maximum Suppression (NMS) algorithm generates object detection and classification output parameters for the AI model by removing overlapping detections and selecting a best set of detections according to a determined confidence score for the detections remaining; and outputting the trained AI model for use with medical image analysis.