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Found 15 papers, 1 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.
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.
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.
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.
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.
A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances
This review summarizes the work of Raman spectroscopy in identifying the composition of substances and reviews the preprocessing process of Raman spectroscopy, the analysis methods and applications of artificial intelligence.
Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network
The scheme first transforms the noisy Raman spectrum to a two-dimensional scale map using CWT.
Noise Reduction Technique for Raman Spectrum using Deep Learning Network
It is shown that output SNR of the proposed noise reduction technology is 10. 24 dB greater than that of the wavelet noise reduction method while the RMSE and the MAPE are 292. 63 and 10. 09, which are much better than the proposed technique.
Method for classifying a noisy Raman spectrum based on a wavelet transform and a deep neural network
The optimum back-end classifier was obtained by testing the ML and DCNN models with several noisy Raman spectrums (10-30 dB noise power).
