公开(公告)号：US12112475B2

公开(公告)日：2024-10-08

申请号：US17659914

申请日：2022-04-20

Applicant: Wuhan University

Inventor： Juan Liu ,  Yuqi Chen ,  Jing Feng

IPC: G06T7/00 ,  G06T7/11 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G16H30/40 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/10056 ,  G06T2207/20036 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06T2207/30068 ,  G06T2207/30096 ,  G06V2201/03

Abstract: Provided is a method and system for predicting tumor mutation burden (TMB) in triple negative breast cancer (TNBC) based on nuclear scores and histopathological whole slide images (WSIs). The method includes the following steps: first, screening the histopathological WSIs of TNBC; calculating a TMB value of each patient according to gene mutation of each patient with TNBC, and dividing the TMB values into two groups with high and low TMB according to a set threshold; dividing the histopathological WSIs of TNBC into patches of a set size; screening a certain number of patches with high nuclear scores according to a nuclear score function; then building a convolutional neural network (CNN) classification model, and stochastically initializing parameters in the CNN classification model; and finally, putting the screened patches into the built CNN classification model for training, so as to automatically predict high or low TMB with the histopathological WSIs of TNBC.

公开(公告)号：US20230153994A1

公开(公告)日：2023-05-18

申请号：US17659914

申请日：2022-04-20

Applicant: Wuhan University

Inventor： Juan Liu ,  Yuqi Chen ,  Jing Feng

IPC: G06T7/00 ,  G06T7/11 ,  G06V10/764 ,  G06V10/82 ,  G06V10/774 ,  G16H50/20 ,  G16H30/40

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06V10/82 ,  G06V10/764 ,  G06V10/774 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/10056 ,  G06T2207/20036 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06T2207/30068 ,  G06T2207/30096 ,  G06V2201/03

Abstract: Provided is a method and system for predicting tumor mutation burden (TMB) in triple negative breast cancer (TNBC) based on nuclear scores and histopathological whole slide images (WSIs). The method includes the following steps: first, screening the histopathological WSIs of TNBC; calculating a TMB value of each patient according to gene mutation of each patient with TNBC, and dividing the TMB values into two groups with high and low TMB according to a set threshold; dividing the histopathological WSIs of TNBC into patches of a set size; screening a certain number of patches with high nuclear scores according to a nuclear score function; then building a convolutional neural network (CNN) classification model, and stochastically initializing parameters in the CNN classification model; and finally, putting the screened patches into the built CNN classification model for training, so as to automatically predict high or low TMB with the histopathological WSIs of TNBC.

公开(公告)号：US12008076B2

公开(公告)日：2024-06-11

申请号：US17546965

申请日：2021-12-09

Applicant: Wuhan University

Inventor： Juan Liu ,  Zhiqun Zuo ,  Yuqi Chen ,  Zhuoyu Li ,  Jing Feng

IPC: G06F18/214 ,  G06F18/241 ,  G06N20/00 ,  G06T7/00 ,  G06T7/194

CPC classification number: G06F18/2148 ,  G06F18/241 ,  G06N20/00 ,  G06T7/0012 ,  G06T7/194 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30068

Abstract: Provided is an end-to-end attention pooling-based classification method for histopathological images obtaining a better classification effect for small number of samples by S1, cutting the histopathology image into patches of a specified size, removing the patches with too much background area and packaging the remaining patches into a bag; S2, training a deep learning network by taking the bag obtained in S1 as an input using a standard multi-instance learning method; S3, scoring all the patches by using the trained deep learning network, and selecting m patches with highest and lowest scores for each whole slide image to form a new bag; S4, building a deep learning network including an attention pooling module, and training the network by using the new bag obtained in S3; and S5, after the histopathology image to be classified is processed in S1 and S3, performing classification by using the model obtained in S4.

公开(公告)号：US20220188573A1

公开(公告)日：2022-06-16

申请号：US17546965

申请日：2021-12-09

Applicant: Wuhan University

Inventor： Juan Liu ,  Zhiqun Zuo ,  Yuqi Chen ,  Zhuoyu Li ,  Jing Feng

IPC: G06K9/62 ,  G06T7/194 ,  G06T7/00 ,  G06N20/00

Abstract: The present disclosure provides an end-to-end attention pooling-based classification method for histopathological images. The method specifically includes the following steps: S1, cutting the histopathology image into patches of a specified size, removing the patches with too much background area and packaging the remaining patches into a bag; S2, training a deep learning network by taking the bag obtained in S1 as an input using a standard multi-instance learning method; S3, scoring all the patches by using the trained deep learning network, and selecting m patches with highest and lowest scores for each whole slide image to form a new bag; S4, building a deep learning network including an attention pooling module, and training the network by using the new bag obtained in S3; and S5, after the histopathology image to be classified is processed in S1 and S3, performing classification by using the model obtained in S4. The present disclosure can obtain a better classification effect under the current situation of only a small number of samples, provide an auxiliary diagnosis mechanism for doctors, and alleviate the problem of shortage of medical resources.