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

Histopathological images are the gold standard for diagnosing liver cancer. However, the accuracy of fully digital diagnosis in computational pathology needs to be improved. In this paper, in order to solve the problem of multi-label and low classification accuracy of histopathology images, we propose a locally deep convolutional Swim framework (LDCSF) to classify multi-label histopathology images. In order to be able to provide local field of view diagnostic results, we propose the LDCSF model, which consists of a Swin transformer module, a local depth convolution (LDC) module, a feature reconstruction (FR) module, and a ResNet module. The Swin transformer module reduces the amount of computation generated by the attention mechanism by limiting the attention to each window. The LDC then reconstructs the attention map and performs convolution operations in multiple channels, passing the resulting feature map to the next layer. The FR module uses the corresponding weight coefficient vectors obtained from the channels to dot product with the original feature map vector matrix to generate representative feature maps. Finally, the residual network undertakes the final classification task. As a result, 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 to calculate the tumor-to-stromal ratio, which lays the foundation for the analysis of the microenvironment of liver cancer histopathological images. Second, we released a multilabel histopathology image of liver cancer, our code and data are available at https://github.com/panliangrui/LSF.

Multi-Head Attention Mechanism Learning for Cancer New Subtypes and Treatment Based on Cancer Multi-Omics Data

Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omics data and clinical features among subtypes of different cancers. Therefore, the identification and discovery of cancer subtypes are crucial for the diagnosis, treatment, and prognosis of cancer. In this study, we proposed a generalization framework based on attention mechanisms for unsupervised contrastive learning (AMUCL) to analyze cancer multi-omics data for the identification and characterization of cancer subtypes. AMUCL framework includes a unsupervised multi-head attention mechanism, which deeply extracts multi-omics data features. Importantly, a decoupled contrastive learning model (DMACL) based on a multi-head attention mechanism is proposed to learn multi-omics data features and clusters and identify new cancer subtypes. This unsupervised contrastive learning method clusters subtypes by calculating the similarity between samples in the feature space and sample space of multi-omics data. Compared to 11 other deep learning models, the DMACL model achieved a C-index of 0.002, a Silhouette score of 0.801, and a Davies Bouldin Score of 0.38 on a single-cell multi-omics dataset. On a cancer multi-omics dataset, the DMACL model obtained a C-index of 0.016, a Silhouette score of 0.688, and a Davies Bouldin Score of 0.46, and obtained the most reliable cancer subtype clustering results for each type of cancer. Finally, we used the DMACL model in the AMUCL framework to reveal six cancer subtypes of AML. By analyzing the GO functional enrichment, subtype-specific biological functions, and GSEA of AML, we further enhanced the interpretability of cancer subtype analysis based on the generalizable AMUCL framework.