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检索条件"任意字段=Conference on Multimodal Image Exploitation and Learning"

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Heart Rate and Oxygen Level Estimation from Facial Videos Using a Hybrid Deep learning Model

Heart Rate and Oxygen Level Estimation from Facial Videos Us...

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conference on multimodal image exploitation and learning

作者： Zheng, Yufeng Univ Mississippi Med Ctr Jackson MS 38677 USA

ISBN: (纸本)9781510673854;9781510673847

Vital signs can be inferred from facial videos for health monitoring remotely, while facial videos can be easily obtained through phone cameras, webcams, or surveillance systems. In this study, we propose a hybrid deep learning model to estimate heart rate (HR) and blood oxygen saturation level (SpO2) from facial videos. The hybrid model has a mixed network architecture consisting of convolutional neural network (CNN), convolutional long short-term memory (convLSTM), and video vision transformer (ViViT). Temporal resolution is emphasized in feature extraction since both HR and SpO2 are varying over time. A clip of video consists of a set of frame images within a time segment. CNN is performed with regard to each frame (e.g., time distributed), convLSTM and ViViT can be configured to process a sequence of frames. These high-resolution temporal features are combined to predict HR and SpO2, which are expected to capture these signal variations. Our vital video dataset is fairly large by including 891 subjects from difference races and ages. Facial detection and data normalization are performed in preprocessing. Our experiments show that the proposed hybrid model can predict HR and SpO2 accurately. In addition, those models can be extended to infer HR fluctuations, respiratory rates, and blood pressure variations from facial videos.

关键词： Vital sign Facial video convolutional neural network (CNN) Convolutional long short-term memory (convLSTM) Video vision transformer (ViViT) Deep learning Telehealth

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Attention-Based Two-Stream High-Resolution Networks for Building Damage Assessment from Satellite imagery

Attention-Based Two-Stream High-Resolution Networks for Buil...

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conference on multimodal image exploitation and learning

作者： Oludare, Victor Kezebou, Landry Jinadu, Obafemi Panetta, Karen Agaian, Sos Tufts Univ Medford MA 02155 USA CUNY New York NY 10021 USA

ISBN: (纸本)9781510650770;9781510650763

Satellite imagery provides an efficient means of assessing and effectively planning search and rescue efforts in the aftermath of disasters such as earthquakes, flooding, tsunamis, wildfires, and conflicts. It enables timely visualization of buildings and the human population affected by these disasters and provides humanitarian organizations with crucial information needed to strategize and deliver the much need aid effectively. Recent research on remote sensing combines machine learning methodologies with satellite imagery to automate information extraction, thus reducing turn-around time and manual labor. The existing state-of-the-art approach for building damage assessment relies on an ensemble of different models to obtain independent predictions that are then aggregated to one final output. Other methods rely on a multi-stage model that involves a building localization module and a damage classification module. These methods are either not end-to-end trainable or are impractical for real-time applications. This paper proposes an Attention-based Two-Stream High-Resolution Network (ATS-HRNet), which unifies the building localization and classification problem in an end-to-end trainable manner. The basic residual blocks in HRNet are replaced with attention-based residual blocks to improve the model's performance. Furthermore, a modified cutmix data augmentation technique is introduced for handling class imbalance in satellite imagery. Experiments show that our approach significantly performs better than the baseline and other state-of-the-art methods for building damage classification.

关键词： building damage assessment convolutional neural network deep learning semi-supervised learning xBD dataset attention module selective oversampling pixel-aware cutmix

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Binary vs. Multi-Class Segmentation for Off-angle Iris images using Deep learning Frameworks

Binary vs. Multi-Class Segmentation for Off-angle Iris Image...

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conference on multimodal image exploitation and learning

作者： Ghandour, Imad El Ddine Karakaya, Mahmut Kennesaw State Univ Dept Comp Sci Marietta GA 30060 USA

ISBN: (纸本)9781510650770;9781510650763

During the pandemic, it is a critical task to recognize individuals and to verify their identity without touching a surface or removing the face mask. Compared with other biometric modalities, iris recognition provides accurate, reliable, and contactless biometrics measure. Traditional iris recognition systems require high quality frontal iris images. The image quality dependency limits its recognition performance in standoff applications. However, standoff biometric systems work in a less controlled environment where the captured images may be nonideal and off-angle. Since segmentation is the first step among recognition tasks, having an accurate segmentation is extremely critical to achieving a high recognition performance especially for off-angle iris images. Recent advances in deep learning enable the usage of some convolutional neural networks (CNN) for the challenging iris segmentation task. During training process, binary iris segmentation masks feed to the CNN framework to learn the iris texture where all other eye structures included in the same class. However, the pupil and sclera segmentation may provide useful additional information for iris segmentation. In this paper, we investigate the CNN-based iris segmentation frameworks for binary segmentation and multi-class segmentation. We first train the deep networks with binary segmentation masks (iris vs. others). Then, additional deep networks are trained with multi-class segmentation masks where pupil, iris texture, sclera, and other eye structures in separate classes. Finally, we compare the segmentation accuracies with off-angle iris images where images are captured from -50 degrees to 50 degrees in angle. Based on the results from real experiments, the proposed method shows effectiveness in segmentation for off-angle iris images.

关键词： biometrics iris recognition segmentation off-angle iris images

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A Machine learning Approach to Predicting New Pharmaceutical Successes in Clinical Trials

A Machine Learning Approach to Predicting New Pharmaceutical...

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conference on multimodal image exploitation and learning

作者： Schiro, Frank Agaian, Sos S. CUNY Grad Ctr New York NY 10016 USA CUNY Dept Comp Sci Coll Staten Isl New York NY 10314 USA

ISBN: (纸本)9781510650770;9781510650763

Drug discovery and development pipelines are complex, long, and depend on several factors, including FDA trials. Recently artificial intelligence has moved from largely theoretical studies to real-world applications. The pharmaceutical industry currently faces challenges sustaining its drug development programs because of increased R&D costs and reduced efficiency. There is a critical need for time and cost-efficient strategies to analyze and interpret these data to advance human drug development prediction. Also, to predict if new drugs will pass FDA trials or not. In this study, we attempt to accomplish four tasks (1) create a reliable dependent variable to categorize drugs with minimal noise, (2) link this dependent variable to predictor variables, (3) utilize a boosted tree model with the principal component method to develop an algorithm to predict FDA trial outcomes, and (4) to develop a design matrix of regressor variables for 3500 approved and investigational drugs built with DrugBank 5.1.8 Drug Targets and Drug Categories data, as well as ATC codes from both DrugBank and ChEMBL databases. Additionally, intensive computer simulations (1) show a 91% prediction success rate over a wide range of drug categories, (2) provide new insights into predicting the success of drug development, and (3) present data that can save time and resources and help decision-making for the benefit of companies future investigation, potentially to help clinical trials and investment.

关键词： Drug development FDA approval drug investment

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Machine learning Based Classification System using Depth-Dependent Variation Encoding for Classifying Cervical Two-Photon Excited Fluorescence image Stacks

Machine Learning Based Classification System using Depth-Dep...

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conference on multimodal image exploitation and learning

作者： Sanghavi, Foram Panetta, Karen Agaian, Sos Polleys, Christopher Hong-Thao Thieu Genega, Elizabeth Georgakoudi, Irene Tufts Univ Dept Elect Engn Medford MA 02155 USA CUNY Dept Comp Sci New York NY USA Dept Biomed Engn Medford MA 02155 USA Tufts Med Ctr Dept Obstet & Gynecol Boston MA 02111 USA Tufts Med Ctr Dept Pathol Boston MA 02111 USA

ISBN: (纸本)9781510650770;9781510650763

The World Health Organization (WHO) called for a global fight against cervical cancer. There are an estimated 569,000 new cases and 310,000 deaths annually. Searching for practical approaches to deal with cervical cancer screening and treatment has been an urgent research subject. One solution could be to use label-free two-photon excited fluorescence (TPEF) imaging to address this need. The colposcopy-guided biopsy method is being used for cervical precancer detection relying primarily on morphological and organization cell and tissue feature changes. However, the overall performance of colposcopy and biopsy remains unsatisfactory. Label-free TPEF provides images with high morphological and functional (metabolic) content and could lead to enhanced detection of cervical pre-cancers. This paper uses the cell texture and morphology features to classify stacks of such TPEF images acquired from freshly excised healthy and pre-cancerous human cervical tissues. Herein, an automated denoising algorithm and a parametrized edge enhancement method is used for pre-processing the images in the stack. The computer simulations performed on the privately available dataset of 10 healthy stacks, 53 precancer stacks, and the recall and specificity of 100 %, respectively, were observed for both texture and morphology features. However, the dataset used to acquire these results is small. The presented model can be used as a base model for further research and analysis of a larger data set to identify early cervical cancerous changes and potentially significantly improve diagnosis and treatment.

关键词： Cervical Cancer detection Two-photon excited fluorescence image machine learning LBP Fibonacci-p patterns depth-dependent variation Edge enhancement denoising optimization

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A Model-based Adaptive Method for Speckle Noise Reduction in Ultrasound images of Ovarian Tumours A New Approach

A Model-based Adaptive Method for Speckle Noise Reduction in...

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conference on multimodal image exploitation and learning

作者： Al-karawi, Dhurgham Ibrahim, Dheyaa Al-Assam, Hisham Du, Hongbo Jassim, Sabah Med Analyt Ltd Ewloe Flint Wales Imam Jaafar Al Sadiq Univ Commun Engn Tech Dept Informat Technol Coll Baghdad Iraq Univ Buckingham Sch Comp Buckingham England

ISBN: (数字)9781510643062

ISBN: (纸本)9781510643062

Ultrasound imaging is widely used in medical diagnostics. The existence of speckle noise tends to impair ultrasound image quality, which has a negative effect on the computer-aided diagnostic pipeline. As a result, a content-preserving noise reduction is an essential part of ultrasound image pre-processing. This paper argues that conventional one-fit-all preprocessing methods on all images irrespective of their quality and/or their content have many limitations. The paper demonstrates that the negative effects of the speckle noise are more significant in regions where solid tissues are present. Consequently, we propose an adaptive approach of using trained classification models to detect such regions within the image and targeting the speckle noise of the detected regions instead of the whole image. The detection is achieved by placing a sliding window over the image and feeding individual windows to a trained classifier. In this study, we first analyse the content of the images to identify the complexity of the speckle noise by training a linear support vector machine classifier on histogram-based measurements such as skewness and kurtosis to determine whether the image partially or fully needs pre-processing. To evaluate the effectiveness of the new adaptive pre-processing methods, a hybrid two-model solution in which the first trainable model decides if an image requires pre-processing or not and applies it respectively on the whole image. The second model takes a step further to check which parts of the images requires pre-processing and adaptively applies it using the block-based trainable system. The results, based on 138 benign and 104 malignant ovarian ultrasound images, show that the two models performed better than other state-of-the-art pre-processing techniques, which confirms the need for the adaptive system that applies pre-processing only when needed.

关键词： Ultrasound image Denoising Speckle noise adaptive model Medical image texture features

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3-D Shape Object Reconstruction with Non-Lambertian Surface from Multiple Views Based on Deep learning

3-D Shape Object Reconstruction with Non-Lambertian Surface ...

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conference on multimodal image exploitation and learning

作者： Voronin, V. Frantc, V Semenishchev, E. Zhdanova, M. Zelensky, A. Agaian, S. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia CUNY Dept Comp Sci New York NY 10021 USA

ISBN: (纸本)9781510650770;9781510650763

The 3-D reconstruction is relative to constructing a mathematical representation of the scene geometric. In most existing approaches, Lambert's law of the object reflectivity in the scene is explicitly or implicitly assumed. In practice, the law of reflectivity of scene objects differs from Lambert's law as, for example, objects with properties: semi-transparent, transparent, specular, with subsurface scattering effects. This paper proposes an algorithm to estimate a surface reflectance model of 3-D shape and parameters from multiple views. The proposed algorithm includes the following steps: 1) determination of the optical properties of the scanned scene by separating the direct and global lighting components using high-frequency templates;2) generation of a set of patterns of structured light, the structure of which depends on the optical properties of the scanned scene;3) scanning the scene using the generated structured light patterns for views non-Lambertian surface;4) construction of a 3-D model of the scene by triangulating methodology. To solve the problem of determining the views non-Lambertian surface, a 3-D reconstruction algorithm based on a convolutional neural network is proposed. To train the neural network, we apply two stages. At the first stage, the encoder is trained for the descriptor description of the input image. In the second step, a fully connected neural network is added to the encoder for regression for choosing the best views. The coder is trained using the generative adversarial methodology to construct a descriptor description that stores spatial information and information about the optical properties of surfaces located in different areas of the image. The codec network is trained to recover the defect map (depends directly on the sensor and scene properties) from a color image. The architecture of the neural network (generator) is based on the U-Net architecture. As a result, this method uses non-Lambertian properties, and it can com

关键词： 3-D reconstruction non-Lambertian surface optical properties deep learning U-Net

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Diagnosing COVID-19 Pneumonia from X-Ray and CT images using Deep learning and Transfer learning Algorithms

Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using...

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conference on multimodal image exploitation and learning

作者： Maghdid, Halgurd S. Asaad, Aras T. Ghafoor, Kayhan Zrar Sadiq, Ali Safaa Mirjalili, Seyedali Khan, Muhammad Khurram Koya Univ Fac Engn Dept Software Engn Koysinjaq Kurdistan Regio Iraq Oxford Ctr Innovat Oxford Drug Design Oxford OX1 1BY England Knowledge Univ Dept Comp Sci Univ PkKirkuk Rd Erbil 44001 Iraq Univ Wolverhampton Sch Math & Comp Sci Wulfruna St Wolverhampton WV1 1LY England Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia King Saud Univ Ctr Excellence Informat Assurance Riyadh Saudi Arabia

ISBN: (数字)9781510643062

ISBN: (纸本)9781510643062

The novel coronavirus 2019 (COVID-19) first appeared in Wuhan province of China and spread quickly around the globe and became a pandemic. The gold standard for confirming COVID-19 infection is through Reverse Transcription-Polymerase Chain Reaction (RT-PCR) assay. The lack of sufficient RT-PCR testing capacity, false negative results of RT-PCR, time to get back the results and other logistical constraints enabled the epidemic to continue to spread albeit interventions like regional or complete country lockdowns. Therefore, chest radiographs such as CT and X-ray can be used to supplement PCR in combating the virus from spreading. In this work, we focus on proposing a deep learning tool that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner. However, the lack of a publicly available dataset of X-ray and CT images makes the design of such AI tools a challenging task. To this end, this study aims to build a comprehensive dataset of X-rays and CT scan images from multiple sources as well as provides a simple but an effective COVID-19 detection technique using deep learning and transfer learning algorithms. In this vein, a simple convolution neural network (CNN) and modified pre-trained AlexNet model are applied on the prepared X-rays and CT scan images. The result of the experiments shows that the utilized models can provide accuracy up to 98% via pre-trained network and 94.1% accuracy by using the modified CNN.

关键词： COVID-19 deep learning coronavirus detection CT scan image CNN

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RESIDUAL ENHANCED MULTI-HYPERGRAPH NEURAL NETWORK

RESIDUAL ENHANCED MULTI-HYPERGRAPH NEURAL NETWORK

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IEEE International conference on image Processing (ICIP)

作者： Huang, Jing Huang, Xiaolin Yang, Jie Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China

ISBN: (纸本)9781665441155

Hypergraphs are a generalized data structure of graphs to model higher-order correlations among entities, which have been successfully adopted into various researching fields. Meanwhile, HyperGraph Neural Network (HGNN) is currently the de-facto method for hypergraph representation learning. However, HGNN aims at single hypergraph learning and uses a pre-concatenation approach when confronting multi-modal datasets, which leads to sub-optimal exploitation of the inter-correlations of multi-modal hypergraphs. HGNN also suffers the over-smoothing issue, that is, its performance drops significantly when layers are stacked up. To resolve these issues, we propose the Residual enhanced Multi-Hypergraph Neural Network, which can not only fuse multi-modal information from each hypergraph effectively, but also circumvent the over-smoothing issue associated with HGNN. We conduct experiments on two 3D benchmarks, the NTU and the ModelNet40 datasets, and compare against multiple state-of-the-art methods. Experimental results demonstrate that both the residual hypergraph convolutions and the multi-fusion architecture can improve the performance of the base model and the combined model achieves a new state-of-the-art. Code is available at https://***/OneForward/ResMHGNN.

关键词： Hypergraph learning multi-hypergraph learning deep hypergraph neural network multimodal fusion 3D object classification

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Exploration of Deep learning-based multimodal Fusion for Semantic Road Scene Segmentation 14

Exploration of Deep Learning-based Multimodal Fusion for Sem...

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14th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)

作者： Zhang, Yifei Morel, Olivier Blanchon, Marc Seulin, Ralph Rastgoo, Mojdeh Sidibe, Desire Univ Bourgogne Franche Comte ERL VIBOT CNRS 6000 ImViA Lab EA 7535 Le Creusot France

ISBN: (纸本)9789897583544

Deep neural networks have been frequently used for semantic scene understanding in recent years. Effective and robust segmentation in outdoor scene is prerequisite for safe autonomous navigation of autonomous vehicles. In this paper, our aim is to find the best exploitation of different imaging modalities for road scene segmentation, as opposed to using a single RGB modality. We explore deep learning-based early and later fusion pattern for semantic segmentation, and propose a new multi-level feature fusion network. Given a pair of aligned multimodal images, the network can achieve faster convergence and incorporate more contextual information. In particular, we introduce the first-of-its-kind dataset, which contains aligned raw RGB images and polarimetric images, followed by manually labeled ground truth. The use of polarization cameras is a sensory augmentation that can significantly enhance the capabilities of image understanding, for the detection of highly reflective areas such as glasses and water. Experimental results suggest that our proposed multimodal fusion network outperforms unimodal networks and two typical fusion architectures.

关键词： Semantic Segmentation multimodal Fusion Deep learning Road Scenes

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