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Integrating Neural Networks and Dictionary learning for Multidimensional Clinical Characterizations from Functional Connectomics data 1

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10th international Workshop on Machine learning in Medical Imaging (MLMI) / 22nd international conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： D'Souza, Niharika Shimona Nebel, Mary Beth Wymbs, Nicholas Mostofsky, Stewart Venkataraman, Archana Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA Kennedy Krieger Inst Ctr Neurodev & Imaging Res Baltimore MD USA Johns Hopkins Sch Med Dept Neurol Baltimore MD USA Johns Hopkins Sch Med Dept Pediat Baltimore MD USA

ISBN: (数字)9783030322489

ISBN: (纸本)9783030322489;9783030322472

We propose a unified optimization framework that combines neural networks with dictionary learning to model complex interactions between resting state functional MRI and behavioral data. The dictionary learning objective decomposes patient correlation matrices into a collection of shared basis networks and subject-specific loadings. These subject-specific features are simultaneously input into a neural network that predicts multidimensional clinical information. Our novel optimization framework combines the gradient information from the neural network with that of a conventional matrix factorization objective. This procedure collectively estimates the basis networks, subject loadings, and neural network weights most informative of clinical severity. We evaluate our combined model on a multi-score prediction task using 52 patients diagnosed with Autism Spectrum Disorder (ASD). Our integrated framework outperforms state-of-the-art methods in a ten-fold cross validated setting to predict three different measures of clinical severity.

关键词： Matrix algebra

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A Novel Framework For Sentiment Analysis Using Deep learning

A Novel Framework For Sentiment Analysis Using Deep Learning

引用

international conference on Advanced Communication Technology (ICACT)

作者： Andleeb Aslam Usman Qamar Pakizah Saqib Reda Ayesha Aiman Qadeer National University of Sciences and Technology (NUST) Islamabad Pakistan

ISBN: (数字)9791188428045

ISBN: (纸本)9781728149431

Large amount of un-structured data is present online in the form of opinions and reviews. The most important task of NLP is to Extract useful information from unstructured data by first converting into structured form. Many customers write down reviews online but do not give rating to them. The main concern of this paper is to perform sentiment analysis by predicting two main types of polarities from reviews available online i-e positive and negative. Neural networks models fail to capture the contextual meaning of words and also fails to save long sequences of words and thus results in reducing performance. To overcome this issue a novel Hybrid model (RNN-LSTM-BiLSTM-CNN) using majority voting, word2vec and pre-trained Glove embedding (100d) is proposed to predict sentiment polarity against each review. Loss function used is Binary cross entropy. The proposed model is tested on different state-of-the-art datasets like SST-1, SST-2 and MR Movie review dataset. Results proved that our proposed model results in improved accuracy.

关键词： Sentiment analysis Machine learning Task analysis Software engineering Motion pictures Analytical models

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Predicting Dropout in Higher Education based on Secondary School Performance 22

Predicting Dropout in Higher Education based on Secondary Sc...

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22nd IEEE international conference on intelligent engineering Systems (INES)

作者： Nagy, Marcell Molontay, Roland Budapest Univ Technol & Econ Dept Stochast MTA BME Stochast Res Grp Budapest Hungary

ISBN: (纸本)9781538611227

Predicting student performance, preventing failure and identifying the factors influencing student dropout are issues that have attracted a great deal of research interest recently. In this study, we employ and evaluate several machine learning algorithms to identify students at-risk and predict student dropout of university programs based on the data available at the time of enrollment (secondary school performance, personal details). We also present a data-driven decision support platform for education directorate and stakeholders. The models are built on data of 15,825 undergraduate students from Budapest University of Technology and Economics enrolled between 2010 and 2017 and finished their undergraduate studies either by graduation or dropping out. We handle the problem of missing data by imputation. After performing feature extraction and feature selection, a wide range of classifiers have been trained including Decision Tree-based algorithms, Naive Bayes, k-NN, Linear Models and Deep learning with different input settings. The methods were tested using 10-fold cross-validation and the AUC of the best models, Gradient Boosted Trees and Deep learning, were 0.808 and 0.811 respectively.

关键词： Education Biological system modeling data models Economics Decision trees Predictive models Mathematics

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Human-in-the-loop design with machine learning 22

Human-in-the-loop design with machine learning

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22nd international conference on engineering Design, ICED 2019

作者： Wang, Pan Peng, Danlin Li, Ling Chen, Liuqing Wu, Chao Wang, Xiaoyi Childs, Peter Guo, Yike Imperial College London United Kingdom University of Kent United Kingdom Zhejiang University China

Deep learning methods have been applied to randomly generate images, such as in fashion, furniture design. To date, consideration of human aspects which play a vital role in a design process has not been given significant attention in deep learning approaches. In this paper, results are reported from a human-in-the-loop design method where brain EEG signals are used to capture preferable design features. In the framework developed, an encoder extracting EEG features from raw signals recorded from subjects when viewing images from ImageNet are learned. Secondly, a GAN model is trained conditioned on the encoded EEG features to generate design images. Thirdly, the trained model is used to generate design images from a person's EEG measured brain activity in the cognitive process of thinking about a design. To verify the proposed method, a case study is presented following the proposed approach. The results indicate that the method can generate preferred designs styles guided by the preference related brain signals. In addition, this method could also help improve communication between designers and clients where clients might not be able to express design requests clearly. © 2019 Design Society. All rights reserved.

关键词： Design

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DeepIGeoS-V2: Deep interactive segmentation of multiple organs from head and neck images with lightweight CNNs 4th

DeepIGeoS-V2: Deep interactive segmentation of multiple orga...

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4th international Workshop on Large-Scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2019, the 1st international Workshop on Hardware Aware learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the 2nd international Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd international conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019

作者： Lei, Wenhui Wang, Huan Gu, Ran Zhang, Shichuan Zhang, Shaoting Wang, Guotai School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Department of Radiation Oncology Sichuan Cancer Hospital and Institute University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783030336417

Accurate segmentation of organs-at-risks (OARs) from Computed Tomography (CT) image is a key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. Convolutional Neural Networks (CNN) have recently become the state-of-the-art automated OARs image segmentation method. However, due to the low contrast of head and neck organism tissues in CT, the fully automatic segmentation may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based multi-organ interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy. We use one CNN to obtain an initial automatic segmentation, on which user interactions are added to indicate mis-segmentation. Another CNN takes as input the user interactions with the initial segmentation and gives a refined result. We propose a dimension separate lightweight network that gives a faster and better dense predictions. In addition, we propose a mis-segmentation-based weighting strategy combined with loss functions to achieve more accurate segmentation. We validated the proposed framework in the context of 3D head and neck organism segmentation from CT images. Experimental results show our method achieves a large improvement from automatic CNNs, and obtains higher accuracy with fewer user interventions and less time compared with traditional interactive segmentation method. © Springer Nature Switzerland AG 2019.

关键词： Deep learning

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Limited-Angle Diffuse Optical Tomography Image Reconstruction Using Deep learning 22nd

Limited-Angle Diffuse Optical Tomography Image Reconstructio...

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10th international Workshop on Machine learning in Medical Imaging (MLMI) / 22nd international conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Ben Yedder, Hanene Shokoufi, Majid Cardoen, Ben Golnaraghi, Farid Hamarneh, Ghassan Simon Fraser Univ Sch Comp Sci Burnaby BC Canada Simon Fraser Univ Sch Mechatron Syst Engn Surrey BC Canada

ISBN: (纸本)9783030322397;9783030322380

Diffuse optical tomography (DOT) leverages near-infrared light propagation through in vivo tissue to assess its optical properties and identify abnormalities such as cancerous lesions. While this relatively new optical imaging modality is cost-effective and non-invasive, its inverse problem (i.e., recovering an image from raw signal measurements) is ill-posed, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. Solving the inverse problem becomes even more challenging in the case of limited-angle data acquisition given the restricted number of sources and sensors, the sparsity of the recovered information, and the presence of noise, representative of real world acquisition environments. Traditional optimization-based reconstruction methods are computationally intensive and thus too slow for real-time imaging applications. We propose a novel image reconstruction method for breast cancer DOT imaging. Our method is highlighted by two components: (i) a deep learning network with a novel hybrid loss, and (ii) a distribution transfer learning module. Our model is designed to focus on lesion specific information and small reconstruction details to reduce reconstruction loss and lesion localization errors. The transfer learning module alleviates the need for real training data by taking advantage of cross-domain learning. Both quantitative and qualitative results demonstrate that the proposed method's accuracy surpasses existing methods' in detecting tissue abnormalities.

关键词： Diffuse optical tomography Inverse problem Reconstruction Deep learning Hybrid loss Transfer learning Fuzzy Jaccard

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Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks 1

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10th international Workshop on Machine learning in Medical Imaging (MLMI) / 22nd international conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Kwon, Gihyun Han, Chihye Kim, Dae-shik Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (数字)9783030322489

ISBN: (纸本)9783030322489;9783030322472

As deep learning is showing unprecedented success in medical image analysis tasks, the lack of sufficient medical data is emerging as a critical problem. While recent attempts to solve the limited data problem using Generative Adversarial Networks (GAN) have been successful in generating realistic images with diversity, most of them are based on image-to-image translation and thus require extensive datasets from different domains. Here, we propose a novel model that can successfully generate 3D brain MRI data from random vectors by learning the data distribution. Our 3D GAN model solves both image blurriness and mode collapse problems by leveraging alpha-GAN that combines the advantages of Variational Auto-Encoder (VAE) and GAN with an additional code discriminator network. We also use the Wasserstein GAN with Gradient Penalty (WGAN-GP) loss to lower the training instability. To demonstrate the effectiveness of our model, we generate new images of normal brain MRI and show that our model outperforms baseline models in both quantitative and qualitative measurements. We also train the model to synthesize brain disorder MRI data to demonstrate the wide applicability of our model. Our results suggest that the proposed model can successfully generate various types and modalities of 3D whole brain volumes from a small set of training data.

关键词： Generative Adversarial Networks MRI data augmentation 3D Image synthesis

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Spatiotemporal Breast Mass Detection Network (MD-Net) in 4D DCE-MRI Images 22nd

Spatiotemporal Breast Mass Detection Network (MD-Net) in 4D ...

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10th international Workshop on Machine learning in Medical Imaging (MLMI) / 22nd international conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Deng, Lixi Tang, Sheng Fu, Huazhu Wang, Bin Zhang, Yongdong Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9783030322519;9783030322502

Automatic mass detection in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) helps to reduce the workload of radiologists and improves diagnostic accuracy. However, most of the existing methods rely on hand-crafted features followed by rule-based or shallow machine learning based detection methods. Due to the limited expressive power of hand-crafted features, the diagnostic performances of existing methods are usually unsatisfactory. In this work, we aim to leverage recent deep learning techniques for breast lesion detection and propose the Spatiotemporal Breast Mass Detection Networks (MD-Nets) to detect the masses in the 4D DCE-MRI images automatically. Simulating the clinical diagnosis process, we initially generate image-based candidates from all individual images and then construct a spatiotemporal 4D data to classify mass by using the convolutional long short-term memory network (ConvLSTM) to incorporate kinetic and spatial characteristics. Moreover, we collect a DCE-MRI dataset containing 21,294 annotated images from 172 studies. In experiments, we achieve an AUC of 0.9163 with a sensitivity of 0.8655 and a specificity of 0.8452, which verifies the effectiveness of our method.

关键词： Deep learning

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Multi-template Based Auto-Weighted Adaptive Structural learning for ASD Diagnosis 10th

Multi-template Based Auto-Weighted Adaptive Structural Learn...

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10th international Workshop on Machine learning in Medical Imaging (MLMI) / 22nd international conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Huang, Fanglin Yang, Peng Huang, Shan Ou-Yang, Le Wang, Tianfu Lei, Baiying Shenzhen Univ Natl Reg Key Technol Engn Lab Med Ultrasound Guangdong Key Lab Biomed Measurements & Ultrasoun Sch Biomed EngnHlth Sci Ctr Shenzhen 518060 Peoples R China Shenzhen Univ Coll Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Coll Informat Engn Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China

ISBN: (纸本)9783030326920;9783030326913

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorder and its diagnosis is still a challenging issue. To handle it, we propose a novel multi-template ensemble classification framework for ASD diagnosis. Specifically, based on different templates, we construct multiple functional connectivity brain networks for each subject using resting-state functional magnetic resonance imaging (rs-fMRI) data and extract features representations from these networks. Then, our auto-weighted adaptive structural learning model can learn the shared similarity matrix by an adaptive process while selecting informative features. In addition, our method can automatically allot optimal weight for each template without extra weights and parameters. Further, an ensemble classification strategy is adopted to get the final diagnosis results. Our extensive experiments conducted on the Autism Brain Imaging data Exchange (ABIDE) database demonstrate that our method can improve ASD diagnosis performance. Additionally, our method can detect the ASD-related biomarkers for further medical analysis.

关键词： Autism spectrum disorder Multi-template Adaptive structure learning Auto-weighted

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dataGrid module for Nette framework 22

DataGrid module for Nette framework

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22nd IEEE international conference on intelligent engineering Systems (INES)

作者： Vokorokos, Liberios Uchnar, Matus Chovancova, Eva Tech Univ Kosice Dept Comp & Informat Letna 9 Kosice 04200 Slovakia

ISBN: (纸本)9781538611227

Handling of large amounts of data in web applications requires extended components, which would be difficult to implement in each application separately. Therefore, it is advantageous to have a universal data grid solution available, that will simplify data handling tasks and application development. The aim of this work is to enhance a data grid chosen based on analysis of existing solutions, design and implement a solution for deficiencies arising from analysis and study of the issue. The solution was then tested with real users using real data.

关键词： data data grid Nette Web application

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