公开(公告)号：US20250022261A1

公开(公告)日：2025-01-16

申请号：US18771443

申请日：2024-07-12

Applicant: CLARIPI INC.

Inventor： Si Hwan KIM ,  Jong Hyo KIM

IPC: G06V10/776 ,  G06V10/26 ,  G06V10/774 ,  G06V20/70

Abstract: Disclosed is a method of auditing of artificial intelligence-based medical image segmentation, including: performing preprocessing to generate a preprocessed segmentation image by receiving an input medical image and an output segmentation image provided from a medical image segmentation device and preprocessing the output segmentation image based on the input medical image; generating a heatmap image to generate a segmentation error heatmap image, which includes a segmentation error region in the preprocessed segmentation image, by inputting the preprocessed segmentation image to a deep learning model trained in advance; calculating an error risk to calculate a segmentation error risk for the segmentation error region based on pixel values of the segmentation error heatmap image; and providing auditing information to provide the auditing information for auditing accuracy of the output segmentation image based on the calculated segmentation error risk to a user.