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Found 5 papers, 5 papers with code
Cervical Optical Coherence Tomography Image Classification Based on Contrastive Self-Supervised Texture Learning
Conclusions: The proposed contrastive-learning-based CADx method outperformed the end-to-end CNN models and provided better interpretability based on texture features, which holds great potential to be used in the clinical protocol of "see-and-treat."
Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations
PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation and learning the dynamic representations of users and items simultaneously on the bipartite graph with a self-attention aggregator.
DAN-SNR: A Deep Attentive Network for Social-Aware Next Point-of-Interest Recommendation
In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner.
Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms
In the ten-fold cross-validation process, the CADx approach, HIENet, achieved a 76. 91 $\pm$ 1. 17% (mean $\pm$ s. d.) classification accuracy for four classes of endometrial tissue, namely normal endometrium, endometrial polyp, endometrial hyperplasia, and endometrial adenocarcinoma.
Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks
Convolutional neural networks are powerful tools for image segmentation and classification.
