公开(公告)号：US12002206B2

公开(公告)日：2024-06-04

申请号：US18332302

申请日：2023-06-09

Applicant: GUILIN UNIVERSITY OF ELECTRONIC TECHNOLOGY

Inventor： Xipeng Pan ,  Xinjun Bian ,  Yinghua Lu ,  Zhenbing Liu ,  Zujun Qin ,  Rushi Lan ,  Huihua Yang ,  Huadeng Wang ,  Lingqiao Li ,  Zimin Wang ,  Jijun Cheng ,  Zhizhen Wang ,  Zhengyun Feng ,  Shilong Song

IPC: G06T7/00 ,  G06T7/10

CPC classification number: G06T7/0012 ,  G06T7/10 ,  G06T2207/20081 ,  G06T2207/20132 ,  G06T2207/30068

Abstract: A system and a method for automatically identifying mitosis in H&E stained breast cancer pathological images are provided, belonging to the technical field of digital image processing, and including an input image preprocessing module: cutting an original picture according to a predetermined patch size, and performing a data enhancement by means of picture flipping, rotation, and the like; and a segmentation module: training a segmentation network by cutting patches in a training set, cutting data of a test set according to a corresponding size and sending to the segmentation network to obtain a patch-level segmentation result, and then reconstructing a segmented result into an image belonging to an original size according to patch coordinate information intercepted in a preprocessing stage of it.

公开(公告)号：US11935279B1

公开(公告)日：2024-03-19

申请号：US18505639

申请日：2023-11-09

Applicant: GUILIN UNIVERSITY OF ELECTRONIC TECHNOLOGY

Inventor： Xipeng Pan ,  Huahu Deng ,  Rushi Lan ,  Zhenbing Liu ,  Lingqiao Li ,  Huadeng Wang ,  Xinjun Bian ,  Yajun An ,  Feihu Hou

IPC: G06V10/778 ,  G06T7/11 ,  G06T7/136 ,  G06T7/194 ,  G06V10/764 ,  G06V10/776 ,  G06V10/86 ,  G06V20/50 ,  G06V20/70 ,  G16H30/40 ,  G16H70/60

CPC classification number: G06V10/778 ,  G06T7/11 ,  G06T7/136 ,  G06T7/194 ,  G06V10/764 ,  G06V10/776 ,  G06V10/86 ,  G06V20/50 ,  G06V20/70 ,  G16H30/40 ,  G16H70/60 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06T2207/30061 ,  G06T2207/30068 ,  G06T2207/30096 ,  G06V2201/03

Abstract: Provided is a weakly supervised pathological image tissue segmentation method based on an online noise suppression strategy, including: acquiring a hematoxylin-eosin (H&E) stained graph, processing the H&E stained graph to obtain a data set, dividing the data set, training a classification network based on a divided data set, and generating a pseudo-label; suppressing a noise existing in the pseudo-label based on the online noise suppression strategy, and training a semantic segmentation network through the pseudo-label after noise suppression and a training set corresponding to the pseudo-label to obtain a prediction result of the semantic segmentation network after the training, and taking the prediction result as a final segmentation result.