English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Histopathological images are the gold standard for diagnosing liver cancer.However, the accuracy of fully digital diagnosis in computational pathologyneeds to be improved. In this paper, in order to solve the problem ofmulti-label and low classification accuracy of histopathology images, wepropose a locally deep convolutional Swim framework (LDCSF) to classifymulti-label histopathology images. In order to be able to provide local fieldof view diagnostic results, we propose the LDCSF model, which consists of aSwin transformer module, a local depth convolution (LDC) module, a featurereconstruction (FR) module, and a ResNet module. The Swin transformer modulereduces the amount of computation generated by the attention mechanism bylimiting the attention to each window. The LDC then reconstructs the attentionmap and performs convolution operations in multiple channels, passing theresulting feature map to the next layer. The FR module uses the correspondingweight coefficient vectors obtained from the channels to dot product with theoriginal feature map vector matrix to generate representative feature maps.Finally, the residual network undertakes the final classification task. As aresult, the classification accuracy of LDCSF for interstitial area, necrosis,non-tumor and tumor reached 0.9460, 0.9960, 0.9808, 0.9847, respectively.Finally, we use the results of multi-label pathological image classification tocalculate the tumor-to-stromal ratio, which lays the foundation for theanalysis of the microenvironment of liver cancer histopathological images.Second, we released a multilabel histopathology image of liver cancer, our codeand data are available at https://github.com/panliangrui/LSF.

LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images