Search Results for author:
Found 15 papers, 5 papers with code
FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival
Integrating the different data modalities of cancer patients can significantly improve the predictive performance of patient survival.
Opportunities and challenges in the application of large artificial intelligence models in radiology
Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development.
SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival
To mitigate the impact of missing features within the modality on prediction accuracy, we devised a convolutional masked autoencoder (CMAE) to process the heterogeneous graph post-feature reconstruction.
ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning
Meanwhile, the ESM-NBR obtains the MCC values for DNA-binding residues prediction of 0. 427 and 0. 391 on two independent test sets, which are 18. 61 and 10. 45% higher than those of the second-best methods, respectively.
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.
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.
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.
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.
One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization
To alleviate the potentially catastrophic forgetting issue in multilingual models, we fix all pre-trained model parameters, insert the parameter-efficient structure adapter, and fine-tune it.
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.
Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma
The study proposes a typical transformer image classification framework by integrating noise reduction convolutional autoencoder and feature cross fusion learning (NRCA-FCFL) to classify osteosarcoma histological images.
Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery
Therefore, we conjecture that the multimodal and local-global combination strategies can be treated as the guideline of multi-task SSL for drug discovery.
FEDI: Few-shot learning based on Earth Mover's Distance algorithm combined with deep residual network to identify diabetic retinopathy
Diabetic retinopathy(DR) is the main cause of blindness in diabetic patients.
Heterogeneous network-based drug repurposing for COVID-19
The Corona Virus Disease 2019 (COVID-19) belongs to human coronaviruses (HCoVs), which spreads rapidly around the world.
