AI Medical Compendium Topic

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Attack to Fool and Explain Deep Networks.

IEEE transactions on pattern analysis and machine intelligence
Deep visual models are susceptible to adversarial perturbations to inputs. Although these signals are carefully crafted, they still appear noise-like patterns to humans. This observation has led to the argument that deep visual representation is misa...

GCMM: graph convolution network based on multimodal attention mechanism for drug repurposing.

BMC bioinformatics
BACKGROUND: The main focus of in silico drug repurposing, which is a promising area for using artificial intelligence in drug discovery, is the prediction of drug-disease relationships. Although many computational models have been proposed recently, ...

Single-Shot Object Detection via Feature Enhancement and Channel Attention.

Sensors (Basel, Switzerland)
Features play a critical role in computer vision tasks. Deep learning methods have resulted in significant breakthroughs in the field of object detection, but it is still an extremely challenging obstacle when an object is very small. In this work, w...

Research and Implementation of Text Generation Based on Text Augmentation and Knowledge Understanding.

Computational intelligence and neuroscience
Text generation has always been limited by the lack of corpus data required for language model (LM) training and the low quality of the generated text. Researchers have proposed some solutions, but these solutions are often complex and will greatly i...

Multi-granularity heterogeneous graph attention networks for extractive document summarization.

Neural networks : the official journal of the International Neural Network Society
Extractive document summarization is a fundamental task in natural language processing (NLP). Recently, several Graph Neural Networks (GNNs) are proposed for this task. However, most existing GNN-based models can neither effectively encode semantic n...

Automatic Segmentation of Standing Trees from Forest Images Based on Deep Learning.

Sensors (Basel, Switzerland)
Semantic segmentation of standing trees is important to obtain factors of standing trees from images automatically and effectively. Aiming at the accurate segmentation of multiple standing trees in complex backgrounds, some traditional methods have s...

Global and Local Feature Reconstruction for Medical Image Segmentation.

IEEE transactions on medical imaging
Learning how to capture long-range dependencies and restore spatial information of down-sampled feature maps are the basis of the encoder-decoder structure networks in medical image segmentation. U-Net based methods use feature fusion to alleviate th...

Item Relationship Graph Neural Networks for E-Commerce.

IEEE transactions on neural networks and learning systems
In a modern e-commerce recommender system, it is important to understand the relationships among products. Recognizing product relationships-such as complements or substitutes-accurately is an essential task for generating better recommendation resul...

Attention-Emotion-Enhanced Convolutional LSTM for Sentiment Analysis.

IEEE transactions on neural networks and learning systems
Long short-term memory (LSTM) neural networks and attention mechanism have been widely used in sentiment representation learning and detection of texts. However, most of the existing deep learning models for text sentiment analysis ignore emotion's m...

Siamese network with a depthwise over-parameterized convolutional layer for visual tracking.

PloS one
Visual tracking is a fundamental research task in vision computer. It has broad application prospects, such as military defense and civil security. Visual tracking encounters many challenges in practical application, such as occlusion, fast motion an...