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Imaging signature of 1p/19q co-deletion status derived via machine learning in lower grade glioma 1

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1st international Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, held in conjunction with the 22nd international conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Rathore, Saima Chaddad, Ahmad Bukhari, Nadeem Haider Niazi, Tamim Center for Biomedical Image Computing and Analytics Perelamn School of Medicine University of Pennsylvania PhiladelphiaPA United States Lady Davis Institute for Medical Research McGill University MontrealQC Canada University of Azad Jammu and Kashmir MuzaffarabadAzad Kashmir Pakistan

ISBN: (数字)9783030401245

ISBN: (纸本)9783030401238

We present a new approach to quantify the co-deletion of chromosomal arms 1p/19q status in lower grade glioma (LGG). Though the surgical biopsy followed by fluorescence in-situ hybridization test is the gold standard currently to identify mutational status for diagnosis and treatment planning, there are several imaging studies to predict the same. Our study aims to determine the 1p/19q mutational status of LGG non-invasively by advanced pattern analysis using multi-parametric MRI. The publicly available dataset at TCIA was used. T1-W and T2-W MRIs of a total 159 patients with grade-II and grade-III glioma, who had biopsy proven 1p/19q status consisting either no deletion (n = 57) or co-deletion (n = 102), were used in our study. We quantified the imaging profile of these tumors by extracting diverse imaging features, including the tumor’s spatial distribution pattern, volumetric, texture, and intensity distribution measures. We integrated these diverse features via support vector machines, to construct an imaging signature of 1p/19q, which was evaluated in independent discovery (n = 85) and validation (n = 74) cohorts, and compared with the 1p/19q status obtained through fluorescence in-situ hybridization test. The classification accuracy on complete, discovery and replication cohorts was 86.16%, 88.24%, and 85.14%, respectively. The classification accuracy when the model developed on training cohort was applied on unseen replication set was 82.43%. Non-invasive prediction of 1p/19q status from MRIs would allow improved treatment planning for LGG patients without the need of surgical biopsies and would also help in potentially monitoring the dynamic mutation changes during the course of the treatment. © Springer Nature Switzerland AG 2020.

关键词： Biopsy

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Classification of Apricot Leaves with Extreme learning machines Using Deep Features 1

Classification of Apricot Leaves with Extreme Learning Machi...

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1st international Informatics and Software Engineering conference, IISEC 2019

作者： Ari, Berna Ari, Ali Sengur, Abdulkadir Tuncer, Seda Arslan Sürgü Çok Programli Anadolu Lisesi Bilişim Teknolojileri Bölümü Malatya Turkey Malatya Turgut Özal Üniversitesi Bilgisayar Teknolojileri Bölümü Malatya Turkey Firat Üniversitesi Elektrik Elektronik Müh. Bölümü Elaziǧ Turkey Firat Üniversitesi Yazilim Mühendisliǧi Bölümü Elaziǧ Turkey

ISBN: (纸本)9781728139920

machine learning and image processing-based classification of automated plant species is significant for plant experts/herbalists. Many studies on the subject have been gained to the literature. Today, researchers have applied deep learning to various image-based object recognition tasks. In this study, the classification of automatic apricot species based on Deep Convolutional Neural Networks (DCNN) has been made. The proposed method used the VGG19 model, a pre-trained DCNN model. Seven different feature vectors were obtained by combining the features obtained from three different fully connected layers in different combinations. These feature vectors were given to the input of Excessive learning machines and seven different apricot types were classified. The highest performance rate was obtained from the fc8 layer as 98.8%, and the lowest performance rate was obtained from the feature vector obtained from the combination of fc6 and fc7 layers as 95.2%. © 2019 IEEE.

关键词： Deep neural networks

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Neural network in a multi-agent system for line detection task in images 30

Neural network in a multi-agent system for line detection ta...

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conference on pattern recognition and Tracking XXX

作者： Babayan, P., V Shubin, N. Yu Ryazan State Radio Engn Univ 59-1 Gagarin St Ryazan 390005 Russia

ISBN: (纸本)9781510626560

Lines are one of the most informative structure elements in any images. For this reason, objects detection and recognition problem are often reduced to edge detection task. Radon transform(1,2) and Hough transform(3) are widely used in straight-line detection. However, these methods allow estimating only the straight line parameters (but not line segment). It is proposed to split the image into square fragments (blocks) in which straight-line segments are detected to solve this problem. A multi-agent system is used to combine segments into curves and drop false detections. The use of artificial neural networks(4) (NN) for programming a part of agent behavior in the multi-agent system is the main theme of this work.

关键词： Line detection image processing Radon transform multi-agent system artificial neural networks machine learning

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Food Photo enhancer of one sample generative adversarial network 1

Food Photo enhancer of one sample generative adversarial net...

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1st ACM international conference on Multimedia in Asia, MMAsia 2019

作者： Wang, Shudan Sun, Liang Dong, Weiming Zhang, Yong Institute of Artificial Intelligence School of Automation and Electrical Engineering University of Science and Technology Beijing China National Laboratory of Pattern Recognition China Tencent Ai Lab China

ISBN: (纸本)9781450368414

image enhancement is an important branch in the field of image processing. A few existing methods leverage Generative Adversarial Networks (GANs) for this task. However, they have several defects when applied to a specific type of images, such as food photo. First, a large set of original-enhanced image pairs are required to train GANs that have millions of parameters. Such image pairs are expensive to acquire. Second, color distribution of enhanced images generated by previous methods is not consistent with the original ones, which is not expected. To alleviate the issues above, we propose a novel method for food photo enhancement. No original-enhanced image pairs are required except only original images. We investigate Food Faithful Color Semantic Rules in Enhanced Dataset Photo Enhancement (Faith-EDPE) and also carefully design a light generator which can preserve semantic relations among colors. We evaluate the proposed method on public benchmark databases to demonstrate the effectiveness of the proposed method through visual results and user studies. © 2019 Association for Computing machinery.

关键词： Generative adversarial networks

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Improving Ancient Cham Glyph recognition from Cham Inscription images Using Data Augmentation and Transfer learning 2nd

Improving Ancient Cham Glyph Recognition from Cham Inscripti...

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2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on image Analysis and processing, ICIAP 2019

作者： Nguyen, Minh-Thang Schweyer, Anne-Valérie Le, Thi-Lan Tran, Thanh-Hai Vu, Hai Computer Vision Department MICA International Research Institute Hanoi University of Science and Technology Hanoi Viet Nam French National Center for Scientific Research Paris France

ISBN: (纸本)9783030307530

Ancient Cham glyphs have mostly appeared in inscriptions on stones at some museums in Vietnam. Unfortunately, these inscriptions are being abrasive by the time. To conserve Cham heritage as well as to make them widely accessible and readable by users, digitization and recognition of ancient Cham glyphs become necessary. In our previous work, we have built the first dataset of champ inscription images, manually segmented them in glyphs and annotated by an ancient Cham expert. We adapted some automatic recognition methods and conducted experiments on the manually denoised dataset. The aim of this paper is to extend that earlier research to work on noising data. To this end, we face two main issues. Firstly, the current pre-built dataset is still small which is usually a main drawback for deep learning based methods. Therefore, some data augmentation techniques will be evaluated and investigated to increase the number and variation of samples in the dataset. Second, even with the augmented dataset, the fact of training a deep model from scratch could be very long and sometimes cannot meet a good local minimum. Therefore, we use a simple transfer learning procedure which inherits knowledge from similar or of the same family language. Experiments on both the raw test set and its denoised version show very promising results and of F1-score on two test sets respectively). © 2019, Springer Nature Switzerland AG.

关键词： image classification

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1st international conference on Artificial Intelligence and Cognitive Computing, AICC 2018

1st International Conference on Artificial Intelligence and ...

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1st international conference on Artificial Intelligence and Cognitive Computing, AICC 2018

ISBN: (纸本)9789811315794

The proceedings contain 68 papers. The special focus in this conference is on Artificial Intelligence and Cognitive Computing. The topics include: Cognitive-based adaptive path planning for mobile robot in dynamic environment;prediction for indian road network images dataset using feature extraction method;Design and simulation of capacitive MEMS accelerometer;bioinformatics and image processing—Detection of plant diseases;hybrid approach for pixel-wise semantic segmentation using SegNet and squeezenet for embedded platforms;software modernization through model transformations;Semi-automatic annotation of images using eye gaze data (SAIGA);optimizing regression test suite reduction;application monitoring—Active learning approach;acquiring best rules that represent datasets;an automated computer vision system for extraction of retail food product metadata;attacks on two-key elliptic curve digital signature algorithm;bioinformatics: An application in information science;leveraging deep learning for anomaly detection in video surveillance;speaker diarization system using hidden Markov toolkit;Survey: Enhanced trust management for improving QoS in MANETs;a generic survey on medical big data analysis using internet of things;mean estimation under post-stratified cluster sampling scheme;privacy-preserving Naive Bayesian classifier for continuous data and discrete data;learning style recognition: A neural network approach;morphological-based localization of an Iris image;an enhanced efficiency of key management method for wireless sensor networks using mobile agents;a survey on emotion’s recognition using internet of things;KAZE feature based passive image forgery detection;efficiency-based analysis of homomorphic encryption implications;Performance and analysis of human attention using single-channel wireless EEG sensor for medical application;smart heartbeat monitoring system using machine learning.

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3D Nanoscale Tracking Data Analysis for Intracellular Organelle Movement using machine learning Approach 1

3D Nanoscale Tracking Data Analysis for Intracellular Organe...

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1st international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Lee, Seohyun Kim, Hyuno Ishikawa, Masatoshi Higuchi, Hideo Univ Tokyo Grad Sch Informat Sci & Technol Dept Creat Informat Bunkyo Ku Hongo 7-3-1 Tokyo Japan Univ Tokyo Grad Sch Informat Sci & Technol Dept Informat Phys & Comp Bunkyo Ku Hongo 7-3-1 Tokyo Japan Univ Tokyo Dept Phys Grad Sch Sci Bunkyo Ku Hongo 7-3-1 Tokyo Japan

ISBN: (纸本)9781538678220

Tracking of intracellular organelle movement such as vesicle includes crucial information in biomedicine. To achieve more accurate three-dimensional localization of the target organelle, superresolution imaging microscopy and image processing methods have been developed and applied to many nanoscale tracking systems. Although such recent advances in microscopy imaging have enabled us to gather a tremendous amount of tracking data, the details of the movement including the interaction between cytoskeletons are not yet fully explained. In the present work, we suggest a machine learning approach to clarify the problem in tracking data analysis, as an initial trial to exploit artificial intelligence in distinguishing and classifying the detail features of the vesicle-cytoskeleton interactions.

关键词： Trajectory Microscopy machine learning Target tracking Data analysis

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Fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep learning 2nd

Fusion of Visual and Anamnestic Data for the Classification ...

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作者： Bonechi, Simone Bianchini, Monica Bongini, Pietro Ciano, Giorgio Giacomini, Giorgia Rosai, Riccardo Tognetti, Linda Rossi, Alberto Andreini, Paolo Department of Information Engineering and Matemathics University of Siena Siena Italy Department of Biotechnology Chemistry and Pharmacy University of Siena Siena Italy Department of Medicine Surgery and Neuroscience University of Siena Siena Italy Department of Medical Biotechnologies University of Siena Siena Italy Department of Information Engineering University of Florence Florence Italy

ISBN: (纸本)9783030307530

Early diagnosis of skin lesions is essential for the positive outcome of the disease, which can only be resolved with surgical treatment. In this manuscript, a deep learning method is proposed for the classification of cutaneous lesions based on their visual appearance and on the patient’s anamnestic data. These include age and gender of the patient and position of the lesion. The classifier discriminates between benign and malignant lesions, mimicking a typical procedure in dermatological diagnostics. Good preliminary results on the ISIC Dataset demonstrate the importance of the information fusion process, which significantly improves the classification accuracy. © 2019, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Currency Identification and Forged Banknote Detection using Deep learning 1

Currency Identification and Forged Banknote Detection using ...

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1st international conference on Innovative Trends and Advances in Engineering and Technology, ICITAET 2019

作者： Jadhav, Megha Sharma, Yogesh Kumar Bhandari, G.M. Research Scholar Pune India JJTU Dept. of Computer Science and Engineering Chudela Jhunjhunu India Engineering BSOITR Dept. Comp. Sci. Wagholi India

ISBN: (纸本)9781728119014

Bank note identification is most important approach based on an image processing method. Many techniques and methods are studies involved in the classification of bank notes from different countries conducted experiments on separated image data sets of each country's. Deep learning is machine learning technique that analyze & learns the features of original note. The most important aspect is to find more essential features by using neural networks. In the era of big data where for any real world application, large amount of data has to be processed, deep learning is the superior techniques. In this research, we studied bank note of various countries by extracting its features in depth and analysis it using deep learning. Our system recommended a deep learning-based algorithm to detect Forged bank note through general scanners that can be used by persons to prevent personal monetary damages caused by fake bank note. © 2019 IEEE.

关键词： Classification (of information)

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A Page-Based Reject Option for Writer Identification in Medieval Books 2nd

A Page-Based Reject Option for Writer Identification in Medi...

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作者： Cilia, Nicole Dalia De stefano, Claudio Fontanella, Francesco Marrocco, Claudio Molinara, Mario Scotto di Freca, Alessandra University of Cassino and Southern Lazio Via G. Di Biasio 43 CassinoFR Italy

ISBN: (纸本)9783030307530

One main goal of paleographers is to identify the different writers who wrote a given manuscript. Recently, paleographers are starting to use digital tools which provide new and more objective ways to analyze ancient documents. On the other hand, in the last few years, deep learning techniques have been applied to many domains and to overcome its requirement of a large amount of labeled data, transfer learning has been used. This latter approach uses previously trained large deep networks as starting points to solve specific classification problems. In this paper, we present a novel approach based on deep transfer learning to implement a reject option for the recognition of the writers in medieval documents. The implemented option is page-based and considers the row labels provided by the trained deep network to estimate the class probabilities. The proposed approach has been tested on a set of digital images from a Bible of the XII century. The achieved results confirmed the effectiveness of the proposed approach. © 2019, Springer Nature Switzerland AG.

关键词： Deep learning

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