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Deep Cytometry [article]

Yueqin Li, Ata Mahjoubfar, Claire Lifan Chen, Kayvan Reza Niazi, Li Pei, Bahram Jalali
2019 arXiv   pre-print
followed by deep learning for finding cancer cells in the blood.  ...  Owing to the abundance of data they generate, time stretch instruments are a natural fit to deep learning classification.  ...  followed by deep learning for finding cancer cells in the blood.  ... 
arXiv:1904.09233v1 fatcat:uqdkmjkdd5fppohwxdtitnwije

A Review on Deep Image Contrast Enhancement

Puspad Kumar Sharma, Nitesh Gupta, Anurag Shrivastava
2020 SMART MOVES JOURNAL IJOSCIENCE  
Deep learning is a machine learning approach which is currently revolutionizing a number of disciplines including image processing and computer vision.  ...  This paper will attempt to apply deep learning to image filtering, specifically low-light image enhancement.  ...  One of the biggest issues in applying deep learning to image processing is how to input the image data into the neural network.  ... 
doi:10.24113/ijoscience.v6i1.258 fatcat:jc6hjlhjnvfrjipiumwciep7cq

Deep Open Snake Tracker for Vessel Tracing [article]

Li Chen, Wenjin Liu, Niranjan Balu, Mahmud Mossa-Basha, Thomas S. Hatsukami, Jenq-Neng Hwang, Chun Yuan
2021 arXiv   pre-print
We propose here a deep learning based open curve active contour model (DOST) to trace vessels in 3D images. Initial curves were proposed from a centerline segmentation neural network.  ...  Vessel tracing by modeling vascular structures in 3D medical images with centerlines and radii can provide useful information for vascular health.  ...  Acknowledgement This work was supported by National Institute of Health under grant R01-NS092207.  ... 
arXiv:2107.09049v1 fatcat:vgq4yc3llbfsffgrrq6sq6vaka

Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry

Yueqin Li, Ata Mahjoubfar, Claire Lifan Chen, Kayvan Reza Niazi, Li Pei, Bahram Jalali
2019 Scientific Reports  
followed by deep learning for finding cancer cells in the blood.  ...  Here we describe a new deep learning pipeline, which entirely avoids the slow and computationally costly signal processing and feature extraction steps by a convolutional neural network that directly operates  ...  This work is partially supported by NantWorks LLC. Y. Li was supported by the China Scholarship Council. B. Jalali would like to thank NVIDIA for the donation of the GPU system.  ... 
doi:10.1038/s41598-019-47193-6 pmid:31366998 pmcid:PMC6668572 fatcat:5seedcq6tnfpbczdtnvjkkbw6a

Spatiotemporal Traffic State Prediction Based on Discriminatively Pre-trained Deep Neural Networks

Mohammed Elhenawy, Hesham Rakha
2017 Advances in Science, Technology and Engineering Systems  
In this paper, we adopted a state-of-the-art machine learning deep neural network and the divide-andconquer approach to model large road stretches.  ...  The proposed approach was used to model 21.1and 30.7-mile stretches of highway along I-15 and I-66, respectively.  ...  ASTESJ ISSN: 2415-6698 The use of deep learning neural networks for traffic state prediction was demonstrated to be promising by Elhenawy and Rakha [4] .  ... 
doi:10.25046/aj020387 fatcat:fl5y7xycqnf55lcyqsbtlagcla

Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel

Jingang Zhu, Yetong Jia, Jincheng Lei, Zishun Liu
2021 Mathematics  
Our results show that the end-to-end deep learning framework can effectively predict the nominal stress–stretch curves of hydrogel within a wide range of mesoscopic network structures, which demonstrates  ...  A deep neural network and a 3D convolutional neural network containing the physical information of the network structural model are implemented to predict the nominal stress–stretch curves of hydrogels  ...  Deep architecture has better learning capability by stacking more layers to extend the depth of the NN.  ... 
doi:10.3390/math9212804 fatcat:ps76gxxp2re4zofi3ojqiqnt6m

Scene Classification Based on a Deep Random-Scale Stretched Convolutional Neural Network

Yanfei Liu, Yanfei Zhong, Feng Fei, Qiqi Zhu, Qianqing Qin
2018 Remote Sensing  
Zou et al. [39] translated the feature selection problem into a feature reconstruction problem based on a deep belief network (DBN) and proposed a deep learning-based method, where the features learned  ...  To solve this problem, scene classification based on a deep random-scale stretched convolutional neural network (SRSCNN) for HSR remote sensing imagery is proposed in this paper.  ...  The main reason for the comparatively high accuracy achieved by SRSCNN when compared with the non-deep learning methods such as BoVW, LDA, and LLC is that SRSCNN, as a deep learning method, can extract  ... 
doi:10.3390/rs10030444 fatcat:osfjuan3gverdiqn6qwjoklrwa

Analysis and Classification of H&E-Stained Oral Cavity Tumour Gradings Using Convolution Neural Network

Prabhakaran Mathialagan, SRM Institute of Science and Technology
2021 International Journal of Intelligent Engineering and Systems  
VGG: VGG is known as very deep convolution neural network introduced by Karen Simony and Andrew Zisserman from oxford university in 2014 and it is announced as best deep learning model.  ...  The decorrelation stretching technique is used to improve the colour channels with high intensity and to highlight each pixel by stretching its colour contrasts [24] .  ... 
doi:10.22266/ijies2021.1031.45 fatcat:36schsapmvd3bcpbwfni5o2s24

A Deep Learning Approach to Non-linearity in Wearable Stretch Sensors

Ben Oldfrey, Richard Jackson, Peter Smitham, Mark Miodownik
2019 Frontiers in Robotics and AI  
Instead of trying to engineer the perfect linear sensor we take a deep learning approach which can cope with non-linearity and yet still deliver reliable results.  ...  In the second stage the data is passed to a Long Short Term Memory Neural Network (LSTM) which is trained using part of the data set.  ...  CONCLUSION We have developed a deep learning method for calibrating highly hysteretic resistive stretch sensors.  ... 
doi:10.3389/frobt.2019.00027 pmid:33501043 pmcid:PMC7805618 fatcat:h3g6dtpb4zakhajqs6fyi3mrna

Deep Manifold Prior [article]

Matheus Gadelha, Rui Wang, Subhransu Maji
2020 arXiv   pre-print
We present a prior for manifold structured data, such as surfaces of 3D shapes, where deep neural networks are adopted to reconstruct a target shape using gradient descent starting from a random initialization  ...  networks.  ...  This work is supported in part by NSF grants #1908669 and #1749833.  ... 
arXiv:2004.04242v1 fatcat:lfqz7abwyfb7jo3uh2mz6m4sse

Learning to Reconstruct Symmetric Shapes using Planar Parameterization of 3D Surface

Hardik Jain, Manuel Wollhaf, Olaf Hellwich
2019 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)  
This representation is encoded with surface information to generate 2D geometry images, which can be conveniently learned using traditional deep neural networks without additional overhead.  ...  To generate a voxel or point cloud representation of 3D shapes these methods required adding an extra dimension to the deep network, to handle 3D data.  ...  Figure 2 . 2 Deep neural network structure used for learning geometry image from the RGB image. the (k +1) th iteration w k+1 ij are modified according to the Equation 4 by the edge stretch σ k ij of the  ... 
doi:10.1109/iccvw.2019.00508 dblp:conf/iccvw/JainWH19 fatcat:c4t5osl2b5bkxdyzldh3xuhfpy

Using deep learning to understand and mitigate the qubit noise environment [article]

David F. Wise, John J. L. Morton, Siddharth Dhomkar
2021 arXiv   pre-print
We demonstrate a neural network based methodology that allows for extraction of the noise spectrum associated with any qubit surrounded by an arbitrary bath, with significantly greater accuracy than the  ...  Here, we propose to address this challenge using deep learning algorithms, leveraging the remarkable progress made in the field of image recognition, natural language processing, and more recently, structured  ...  ACKNOWLEDGEMENTS We thank Prasanna Vaidya, Gyde, India, for the fruitful discussions on neural networks.  ... 
arXiv:2005.01144v2 fatcat:g3eslwycjrg5rckvxz4locoqma

HorizonNet: Learning Room Layout With 1D Representation and Pano Stretch Data Augmentation

Cheng Sun, Chi-Wei Hsiao, Min Sun, Hwann-Tzong Chen
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
We also propose Pano Stretch Data Augmentation, which can diversify panorama data and be applied to other panorama-related learning tasks.  ...  Our approach shows good performance on general layouts by qualitative results and cross-validation.  ...  Acknowledgement: This research was partially supported by iStaging and by MOST grants 106-2221-E-007-080-MY3, 107-2218-E-007-047, and 108-2634-F-001-007.  ... 
doi:10.1109/cvpr.2019.00114 dblp:conf/cvpr/SunHSC19 fatcat:rfx3y6b3qbgs5dt7akmfmedeyq

Inexpensive surface electromyography sleeve with consistent electrode placement enables dexterous and stable prosthetic control through deep learning [article]

Jacob A. George, Anna Neibling, Michael D. Paskett, Gregory A. Clark
2020 arXiv   pre-print
These results suggest that deep learning with a 74-layer neural network can substantially improve the dexterity and stability of myoelectric prosthetic control, and that deep-learning techniques can be  ...  The dexterity of conventional myoelectric prostheses is limited in part by the small datasets used to train the control algorithms.  ...  Deep learning substantially improves prosthetic control We hypothesized that additional training data would improve neural network performance.  ... 
arXiv:2003.00070v1 fatcat:bg3ajdhyirgbno6pv2z3nol7ky

HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation [article]

Cheng Sun, Chi-Wei Hsiao, Min Sun, Hwann-Tzong Chen
2019 arXiv   pre-print
We also propose Pano Stretch Data Augmentation, which can diversify panorama data and be applied to other panorama-related learning tasks.  ...  Our approach shows good performance on general layouts by qualitative results and cross-validation.  ...  Acknowledgement: This research was partially supported by iStaging and by MOST grants 106-2221-E-007-080-MY3, 107-2218-E-007-047, and 108-2634-F-001-007.  ... 
arXiv:1901.03861v2 fatcat:juzkyjk34vamvda3hfelevxzrm
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