Wuhan University

Changjiang River Scientific Research Institute, Changjiang Water Resources Commission

Jing Feng

Juan Liu

Zhuoyu Li

Zhiqun Zuo

Yuqi Chen

Liang Chen

Zhen Li

Jian Zhang

Wei Han

Chengjing Xiao

Tao Wei

Xiaomei Shao

Lingmin Liao

Zaiqin Wang

Da Zhang

Huaquan Yang

Xiaohu Yan

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.

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)

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.

End-to-end attention pooling-based classification method for histopathology images

An automatic classification method of whole slide images (WSIs) for cervical tissue pathology based on confidence coefficient selection. The automatic classification method includes steps: S1: dividing the WSIs for the cervical tissue pathology into small pieces having set size, gathering the small pieces of each WSI into a packet, and removing blank pieces in the packets; S2: building a deep CNN model; S3: training the deep CNN for designated rounds; S4: performing sequential arrangement and connection to obtain feature vectors of WSIs by using the trained deep CNN as the feature extractor; S5: training a support vector machine classifier; and S6: processing the WSIs for the cervical tissue pathology, to be classified, through step S1 and step S4 to obtain the feature vectors of the images, and inputting the feature vectors into the trained support vector machine classifier to realize classification.

Automatic classification method of whole slide images of cervical tissue pathology based on confidence coefficient selection

A coating material, including a bottom layer, a middle layer and a surface layer. The bottom layer is an epoxy mortar having a thickness of between 1 and 3 mm, the middle layer is an epoxy resin adhesive having a thickness of between 0.1 and 0.5 mm, and the surface layer is a nanomaterial-modified polyaspartic having a thickness of between 0.3 and 0.5 mm. The epoxy resin adhesive has a viscosity of between 50 and 200 mPa&#xb7;s.

Abrasion-resistant coating material and method of using the same

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Wuhan, China

Jing Feng