Search Results for author:
Found 9 papers, 2 papers with code
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai
We first identify contexts that share similar user preferences with the target context and then locate the corresponding PoIs based on these identified contexts.
CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images
Specifically, CVFC is a three-branch joint framework composed of two Resnet38 and one Resnet50, and the independent branch multi-scale integrated feature map to generate a class activation map (CAM); in each branch, through down-sampling and The expansion method adjusts the size of the CAM; the middle branch projects the feature matrix to the query and key feature spaces, and generates a feature space perception matrix through the connection layer and inner product to adjust and refine the CAM of each branch; finally, through the feature consistency loss and feature cross loss to optimize the parameters of CVFC in co-training mode.
LDCSF: Local depth convolution-based Swim framework for classifying 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.
PACS: Prediction and analysis of cancer subtypes from multi-omics data based on a multi-head attention mechanism model
Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omic data and clinical characteristics among different cancer subtypes.
Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems.
DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data
In this study, we proposed a generalization framework based on attention mechanisms for unsupervised contrastive learning to analyze cancer multi-omics data for the identification and characterization of cancer subtypes.
MGTUNet: An new UNet for colon nuclei instance segmentation and quantification
Cellular nuclei instance segmentation and classification, and nuclear component regression tasks can aid in the analysis of the tumor microenvironment in colon tissue.
A review of machine learning approaches, challenges and prospects for computational tumor pathology
Computational pathology is part of precision oncology medicine.
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation.
