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检索条件"任意字段=2nd Workshop on Machine Learning Methods in Visualisation for Big Data, MLVis 2019"

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machine learning methods in visualisation for big data, mlvis 2019

Machine Learning Methods in Visualisation for Big Data, MLVi...

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2nd workshop on machine learning methods in visualisation for big data, mlvis 2019

ISBN: (纸本)9783038680895

The proceedings contain 4 papers. The topics discussed include: on KDE-based brushing in scatterplots and how it compares to CNN-based brushing;interpreting black-box semantic segmentation models in remote sensing applications;visual analysis of multivariate urban traffic data resorting to local principal curves;and visual ensemble analysis to study the influence of hyper-parameters on training deep neural networks.

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On KDE-based brushing in scatterplots and how it compares to CNN-based brushing 2

On KDE-based brushing in scatterplots and how it compares to...

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2nd workshop on machine learning methods in visualisation for big data, mlvis 2019

作者： Fan, Chaoran Hauser, Helwig University of Bergen Norway

ISBN: (纸本)9783038680895

In this paper, we investigate to which degree the human should be involved into the model design and how good the empirical model can be with more careful design. To find out, we extended our previously published Mahalanobis brush (the best current empirical model in terms of accuracy for brushing points in a scatterplot) by further incorporating the data distribution information that is captured by the kernel density estimation (KDE). Based on this work, we then include a short discussion between the empirical model, designed in detail by an expert and the deep learning-based model that is learned from user data directly. Copyright © 2019 by the Eurographics Association.

关键词： Deep learning

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Interpreting Black-Box Semantic Segmentation Models in Remote Sensing Applications 2

Interpreting Black-Box Semantic Segmentation Models in Remot...

引用

2nd workshop on machine learning methods in visualisation for big data, mlvis 2019

作者： Janik, A. Sankaran, K. Ortiz, A. Montreal Institute of Learning Algorithms University of Texas - El Paso

ISBN: (纸本)9783038680895

In the interpretability literature, attention is focused on understanding black-box classifiers, but many problems ranging from medicine through agriculture and crisis response in humanitarian aid are tackled by semantic segmentation models. The absence of interpretability for these canonical problems in computer vision motivates this study. In this study we present a usercentric approach that blends techniques from interpretability, representation learning, and interactive visualization. It allows to visualize and link latent representation to real data instances as well as qualitatively assess strength of predictions. We have applied our method to a deep learning model for semantic segmentation, U-Net, in a remote sensing application of building detection. This application is of high interest for humanitarian crisis response teams that rely on satellite images analysis. Preliminary results shows utility in understanding semantic segmentation models, demo presenting the idea is available online. Copyright © 2019 by the Eurographics Association.

关键词： Visualization

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A Novel Deep Convolutional Neural Network Structure for Off-line Handwritten Digit Recognition 19

A Novel Deep Convolutional Neural Network Structure for Off-...

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2nd International Conference on big data Technologies (ICBDT) / 3rd International Conference on Business Information Systems workshop (ICBIS)

作者： Wen, Yan Shao, Yi Zheng, Dabo Suzhou Univ Sch Mech & Elect Engn Suzhou Peoples R China Chinese Acad Sci Inst Intelligent Machine Hefei Peoples R China SAIC Gen Motors Norsom Co Ltd Shenyang Peoples R China

ISBN: (纸本)9781450371926

Handwritten digit recognition is widely used nowadays because traditional methods require the extraction of feature vectors from the original data by hand. The deep convolutional neural network (DCNN) can solve this problem efficiently, and the recognition effects are better than the traditional methods. However, deep learning requires lots of data set to be trained, and a suitable learning algorithm is required to adjust a lot of parameters. We analyze the difference between convolution neural network (CNN) network and traditional neural network (NN) in the recognition of handwritten digital datasets. CNN show the obvious superiority to the digital prediction rate, and then we study the applicability of DCNN in handwritten digital datasets. A deep convolutional neural network (DCNN) based on Alex network (AlexNet) is designed and the recognition accuracy can reach to 99%. The confusing digital set are sort out, called Confuse Numbers (CN) based on MNIST. In the field of handwritten digit recognition, most of the work is focusing on the processing of the whole MNIST data set. As a supplement, the CN dataset is helpful to make the progress on handwritten digit recognition technology.

关键词： handwritten digit recognition deep learning CNN feature vectors AlexNet MNIST

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Faster R-CNN based Robust Circulating Tumor Cells Detection with Improved Sensitivity 19

Faster R-CNN based Robust Circulating Tumor Cells Detection ...

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2nd International Conference on big data Technologies (ICBDT) / 3rd International Conference on Business Information Systems workshop (ICBIS)

作者： Liu, Yunxia Si, Hongchao Chen, Yingjie Chen, Yuehui Univ Jinan Based Intelligent Comp Shandong Prov Key Lab Network Jinan Peoples R China Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Shandong Univ Hosp 2 Jinan Shandong Peoples R China

ISBN: (纸本)9781450371926

Automatic detection and numeration of circulating tumor cells (CTCs) in scanned microscopic images of peripheral blood provides effective auxiliary means for clinical diagnosis of cancer, individualized treatment, prognosis judgement evaluation and so on. There have been some work aims at reducing the subjectivity and labor intensity of cytologists with machine learning methods. In this paper, a robust CTCs detection algorithm based on improved Faster R-CNN network is proposed. Special efforts have been devoted to detection of single CTC for improved sensitivity, which is more favorable in real applications. Firstly, a whole slide image preprocessing algorithm is proposed based on statistical analysis of the self-constructed CTCs database. Then, an anchor adjustment and extension method is proposed to alleviate the imbalance between limited number of positive CTCs examples and huge amount of negative normal cells. Experimental results carried out on the self-established CTCs database show that the proposed algorithm demonstrates improved performance in CTCs detection.

关键词： Image Cytometry Circulating Tumor Cells Abnormal Cell Detection Deep learning Faster R-CNN

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