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Computer Graphics

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Ligand-Based Virtual Screening Based on the Graph Edit Distance.

International journal of molecular sciences
Chemical compounds can be represented as attributed graphs. An attributed graph is a mathematical model of an object composed of two types of representations: nodes and edges. Nodes are individual components, and edges are relations between these com...

Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain.

Scientific reports
Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data inform...

NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks.

IEEE transactions on visualization and computer graphics
Existing research on making sense of deep neural networks often focuses on neuron-level interpretation, which may not adequately capture the bigger picture of how concepts are collectively encoded by multiple neurons. We present Neurocartography, an ...

3D Virtual Modeling Realizations of Building Construction Scenes via Deep Learning Technique.

Computational intelligence and neuroscience
The architectural drawings of traditional building constructions generally require some design knowledge of the architectural plan to be understood. With the continuous development of the construction industry, the use of three-dimensional (3D) virtu...

An Improved COVID-19 Detection using GAN-Based Data Augmentation and Novel QuNet-Based Classification.

BioMed research international
COVID-19 is a fatal disease caused by the SARS-CoV-2 virus that has caused around 5.3 Million deaths globally as of December 2021. The detection of this disease is a time taking process that have worsen the situation around the globe, and the disease...

FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches.

IEEE transactions on visualization and computer graphics
The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data-including complex feature engineering processes-to the presentation and improvement of results, with various algorithm...

VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks.

IEEE transactions on visualization and computer graphics
The rapid development of Convolutional Neural Networks (CNNs) in recent years has triggered significant breakthroughs in many machine learning (ML) applications. The ability to understand and compare various CNN models available is thus essential. Th...

Visualizing Graph Neural Networks With CorGIE: Corresponding a Graph to Its Embedding.

IEEE transactions on visualization and computer graphics
Graph neural networks (GNNs) are a class of powerful machine learning tools that model node relations for making predictions of nodes or links. GNN developers rely on quantitative metrics of the predictions to evaluate a GNN, but similar to many othe...

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends.

IEEE transactions on pattern analysis and machine intelligence
High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imag...

DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization.

IEEE transactions on visualization and computer graphics
Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research. However, existing survey articles on AI+VIS focus on visual analytics and information visualization, not scientific visualization (SciVis)...