2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)最新文献

Weakly Supervised Deep Learning for Detecting and Counting Dead Cells in Microscopy Images 弱监督深度学习用于显微镜图像中死亡细胞的检测和计数

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00282

Siteng Chen, Ao Li, Kathleen Lasick, Julie M. Huynh, Linda S. Powers, Janet Roveda, A. Paek

{"title":"Weakly Supervised Deep Learning for Detecting and Counting Dead Cells in Microscopy Images","authors":"Siteng Chen, Ao Li, Kathleen Lasick, Julie M. Huynh, Linda S. Powers, Janet Roveda, A. Paek","doi":"10.1109/ICMLA.2019.00282","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00282","url":null,"abstract":"Counting dead cells is a key step in evaluating the performance of chemotherapy treatment and drug screening. Deep convolutional neural networks (CNNs) can learn complex visual features, but require massive ground truth annotations which is expensive in biomedical experiments. Counting cells, especially dead cells, with very few ground truth annotations remains unexplored. In this paper, we automate dead cell counting using a weakly supervised strategy. We took advantage of the fact that cell death is low before chemotherapy treatment and increases after treatment. Motivated by the contrast, we first design image level supervised only classification neural networks to detect dead cells. Based on the class response map in classification networks, we calculate a Dead Confidence Map (DCM) to specify confidence of each dead cell. Associated with peak clustering, local maximums in the DCM are used to count the number of dead cells. In addition, a biological experiment based weakly supervised data preparation strategy is proposed to minimize human intervention. We show classification performance compared to general purpose and cell classification networks, and report results for the image-level supervised counting task.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114998904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Fusing Visual and Textual Information to Determine Content Safety 融合视觉和文本信息以确定内容安全性

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00324

Rodrigo Leonardo, Amber Hu, M. Uzair, Qiujing Lu, Iris Fu, Keishin Nishiyama, Sooraj Mangalath Subrahmannian, D. Ravichandran

{"title":"Fusing Visual and Textual Information to Determine Content Safety","authors":"Rodrigo Leonardo, Amber Hu, M. Uzair, Qiujing Lu, Iris Fu, Keishin Nishiyama, Sooraj Mangalath Subrahmannian, D. Ravichandran","doi":"10.1109/ICMLA.2019.00324","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00324","url":null,"abstract":"In advertising, identifying the content safety of web pages is a significant concern since advertisers do not want brands to be associated with threatening content. At the same time, publishers would like to maximize the number of web pages on which they can place ads. Thus, a fine balance must be achieved while classifying content safety in order to satisfy both advertisers and publishers. In this paper, we propose a multimodal machine learning framework that fuses visual and textual information from web pages to improve current predictions of content safety. The primary focus is on late fusion, which involves combining final model outputs of separate modalities, such as images and text, to arrive at a single decision. This paper presents a fully automated machine learning framework that performs binary and multilabel classification using late fusion techniques. We also introduce additional work in early fusion, which involves extracting and fusing intermediate features from the two separate models. Our algorithms are applied to data extracted from relevant web pages in the advertising industry. Both of our late and early fusion methods obtain significant improvements over algorithms currently in use.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"410 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115231710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Multiscale Geometric Data Analysis via Laplacian Eigenvector Cascading 基于拉普拉斯特征向量级联的多尺度几何数据分析

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00183

Joshua L. Mike, Jose A. Perea

引用次数: 1

Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB 基于GTSRB的CNN优化的高效进化架构搜索

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00018

Fabio Marco Johner, J. Wassner

引用次数: 9

Fuzzy-Rough Cognitive Networks: Building Blocks and Their Contribution to Performance 模糊-粗糙认知网络:构建模块及其对性能的贡献

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00159

M. Vanloffelt, G. Nápoles, K. Vanhoof

引用次数: 2

An Analysis of Univariate and Multivariate Electrocardiography Signal Classification 单因素和多因素心电图信号分类分析

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00074

Nelly Elsayed, A. Maida, M. Bayoumi

引用次数: 5

MobileNet-Tiny: A Deep Neural Network-Based Real-Time Object Detection for Rasberry Pi MobileNet-Tiny:基于深度神经网络的Rasberry Pi实时目标检测

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00118

Nithesh Singh Sanjay, A. Ahmadinia

引用次数: 6

Radar-Based Non-intrusive Fall Motion Recognition using Deformable Convolutional Neural Network 基于雷达的可变形卷积神经网络非侵入式跌倒运动识别

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00279

Y. Shankar, Souvik Hazra, Avik Santra

{"title":"Radar-Based Non-intrusive Fall Motion Recognition using Deformable Convolutional Neural Network","authors":"Y. Shankar, Souvik Hazra, Avik Santra","doi":"10.1109/ICMLA.2019.00279","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00279","url":null,"abstract":"Radar is an attractive sensing technology for remote and non-intrusive human health monitoring and elderly fall detection due to its ability to work in low lighting conditions, its invariance to the environment, and its ability to operate through obstacles. Radar reflections from humans produce unique micro-Doppler signatures that can be used for classifying human activities and fall motion. However, radar-based elderly fall detection need to handle the indistinctive inter-class differences and large intra-class variations of human fall-motion in a real-world situation. Further, the radar placement in the room and varying aspect angle of the falling subject could result in differing radar micro-Doppler signature of human fall-motion. In this paper, we use a compact short-range 60-GHz frequency modulated continuous wave radar for detecting human fall motion using a novel deformable deep convolutional neural network with novel 1-class contrastive loss function in conjunction to focus loss to recognize elderly fall and address several of these signal processing system challenges. We demonstrate the performance of our proposed system in laboratory conditions under staged fall motion.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125894594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

A Fast and Light Weight Deep Convolution Neural Network Model for Cancer Disease Identification in Human Lung(s) 一种用于人体肺部肿瘤疾病识别的快速轻量级深度卷积神经网络模型

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00225

Siva Skandha Sanagala, S. Gupta, V. K. Koppula, M. Agarwal

引用次数: 8

Encoding in Neural Networks 神经网络编码

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00065

S. Bharitkar

{"title":"Encoding in Neural Networks","authors":"S. Bharitkar","doi":"10.1109/ICMLA.2019.00065","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00065","url":null,"abstract":"Data transforms, parameter re-normalization, and activation functions have gained significant attention in the neural network community over the past several years for improving convergence speed. The results in the literature are for computer vision applications, with batch-normalization (BN) and the Rectified Linear Unit (ReLU) activation attracting attention. In this paper, we present a new approach in data transformation in the context of regression during the synthesis of Head-related Transfer Functions (HRTFs) in the field of audio. The encoding technique whitens the real-valued input data delivered to the first hidden layer of a fully-connected neural network (FCNN) thereby providing the training speedup. The experimental results demonstrate, in a statistically significant way, that the presented data encoding approach outperforms other forms of normalization in terms of convergence speed, lower mean-square error, and robustness to network parameter initialization. Towards this, we used some popular first-and second-order gradient techniques such as scaled conjugate gradient, Extreme Learning Machine (ELM), and stochastic gradient descent with momentum and batch normalization. The improvements, as shown through t-SNE based depiction and analysis on the input covariance matrix, confirm the reduction in the condition number of the input covariance matrix (a process similar to whitening).","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116124697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1