Deep-Learning-Assisted Volume Visualization
Hsueh-Chien Cheng,Antonio Cardone,Somay Jain,Eric Krokos,Kedar Narayan,Sriram Subramaniam,Amitabh Varshney +6 more
43
TL;DR: A new technique that uses spectral methods to facilitate user interactions with high-dimensional features and a new deep-learning-assisted technique for hierarchically exploring a volumetric dataset are presented.
read more
Abstract: Designing volume visualizations showing various structures of interest is critical to the exploratory analysis of volumetric data. The last few years have witnessed dramatic advances in the use of convolutional neural networks for identification of objects in large image collections. Whereas such machine learning methods have shown superior performance in a number of applications, their direct use in volume visualization has not yet been explored. In this paper, we present a deep-learning-assisted volume visualization to depict complex structures, which are otherwise challenging for conventional approaches. A significant challenge in designing volume visualizations based on the high-dimensional deep features lies in efficiently handling the immense amount of information that deep-learning methods provide. In this paper, we present a new technique that uses spectral methods to facilitate user interactions with high-dimensional features. We also present a new deep-learning-assisted technique for hierarchically exploring a volumetric dataset. We have validated our approach on two electron microscopy volumes and one magnetic resonance imaging dataset.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization
Li Guo,Shaojie Ye,Jun Han,Hao Zheng,Han Gao,Danny Z. Chen,Jian-Xun Wang,Chaoli Wang +7 more
- 01 Jun 2020
TL;DR: SSR-VFD is the first work that advocates a machine learning approach to generate high-resolution vector fields from low-resolution ones, and lies in the use of three separate neural nets that take the three components of a low- Resolution vector field as input and jointly output a synthesized high- resolution vector field.
73
A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation
TL;DR: This paper constructs a large fluid flow data set and applies it to a deep learning problem in scientific visualization, improving the accuracy of flow map interpolations, allowing a more precise flow analysis at a reduced memory IO footprint.
41
DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization
TL;DR: In this article , the authors survey related deep learning works in SciVis, specifically in the direction of DL4SciVis: designing DL solutions for solving SciVis problems, focusing on scalar and vector field data but exclude mesh data.
Deep learning for 3D imaging and image analysis in biomineralization research.
TL;DR: This primer will expand the circle of users of deep learning amongst biomineralization researchers and other life scientists involved with 3D imaging, and will encourage incorporation of this powerful tool into their professional skillsets and to explore it further.
25
DeepSI: Interactive Deep Learning for Semantic Interaction
Yali Bian,Chris North +1 more
- 14 Apr 2021
TL;DR: DeepSIfinetune as discussed by the authors integrates deep learning into the human-in-the-loop interactive sense-making pipeline, with two important properties: first, deep learning extracts meaningful representations from raw data, which improves semantic interaction inference.
References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
•Posted Content
Deep Residual Learning for Image Recognition
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
117.9K
•Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
- 04 Sep 2014
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
102.6K
•Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
- 03 Dec 2012
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
- 07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).