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3d-CNN-based feature extraction of ground-based cloud images for direct normal irradiance prediction

SOLAR ENERGY 2019年 181卷 510-518页

作者： Zhao, Xin Wei, Haikun Wang, Hai Zhu, Tingting Zhang, Kanjian Southeast Univ Sch Automat Minist Educ Key Lab Measurement & ControlCSE Nanjing 210096 Jiangsu Peoples R China St Marys Univ Sobey Sch Business Halifax NS Canada

Cloud cover and cloud motion have a large impact on solar irradiance. One of the effective ways for direct normal irradiance (dNI) prediction is to use cloud features, which has been extensively studied. A convolutional neural network (CNN) has the advantage of automatic features extraction by using strong computing capabilities. In this paper, a novel 3d-CNN method is proposed by processing multiple consecutive ground-based cloud (GBC) images in order to extract cloud features including texture and temporal information. The resulting features and the dNI data are then used to establish a dNI forecasting model. The experiments are carried out to evaluate the performance of the proposed forecasting method by using the data from January 1, 2013 to december 31, 2014. The experimental results show that the proposed method coupled with the multilayer perceptron (MLP) model achieves forecast skill of 17.06% for 10-minute ahead dNI prediction.

关键词： direct normal irradiance 3d convolutional neural network Feature extraction Ground-based cloud image

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Automatic classification of lung nodule candidates based on a novel 3d convolution network and knowledge transferred from a 2d network

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MEdICAL PHYSICS 2019年第12期46卷 5499-5513页

作者： Zuo, Wangxia Zhou, Fuqiang He, Yuzhu Li, Xiaosong Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100083 Peoples R China Univ South China Coll Elect Engn Hengyang 421001 Hunan Peoples R China

ObjectiveIn the automatic lung nodule detection system, the authenticity of a large number of nodule candidates needs to be judged, which is a classification task. However, the variable shapes and sizes of the lung nodules have posed a great challenge to the classification of candidates. To solve this problem, we propose a method for classifying nodule candidates through three-dimensional (3d) convolution neural network (ConvNet) model which is trained by transferring knowledge from a multiresolution two-dimensional (2d) ConvNet model. MethodsIn this scheme, a novel 3d ConvNet model is preweighted with the weights of the trained 2d ConvNet model, and then the 3d ConvNet model is trained with 3d image volumes. In this way, the knowledge transfer method can make 3d network easier to converge and make full use of the spatial information of nodules with different sizes and shapes to improve the classification accuracy. ResultsThe experimental results on 551 065 pulmonary nodule candidates in the LUNA16 dataset show that our method gains a competitive average score in the false-positive reduction track in lung nodule detection, with the sensitivities of 0.619 and 0.642 at 0.125 and 0.25 FPs per scan, respectively. ConclusionsThe proposed method can maintain satisfactory classification accuracy even when the false-positive rate is extremely small in the face of nodules of different sizes and shapes. Moreover, as a transfer learning idea, the method to transfer knowledge from 2d ConvNet to 3d ConvNet is the first attempt to carry out full migration of parameters of various layers including convolution layers, full connection layers, and classifier between different dimensional models, which is more conducive to utilizing the existing 2d ConvNet resources and generalizing transfer learning schemes.

关键词： 2d network 3d convolutional neural network automatic classification knowledge transfer lung nodule candidates

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Fall detection using optical level anonymous image sensing system

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OPTICS ANd LASER TECHNOLOGY 2019年 110卷 44-61页

作者： Ma, Chao Shimada, Atsushi Uchiyama, Hideaki Nagahara, Hajime Taniguchi, Rin-ichiro Kyushu Univ Grad Sch Informat Sci & Elect Engn Nishi Ku 744 Motooka Fukuoka Fukuoka 8190395 Japan Osaka Univ Inst Databil Sci 2-8 Yamadaoka Suita Osaka 5650871 Japan

Fall is one of the leading causes of injury for the elderly individuals. Systems that automatically detect falls can significantly reduce the delay of assistance. Most of commercialized fall detection systems are based on wearable devices, which elderly individuals tend to forget wearing. Using surveillance cameras to detect falls based on computer vision is ideal, because anyone in the monitoring scopes can be under protection. However, the privacy protection issue using surveillance cameras has been bothering people. To effectively protect the privacy, we proposed an optical level anonymous image sensing system, which can protect the privacy by hiding the facial regions optically at the video capturing phase. We apply the system to fall detection. In detecting falls, we propose a neural network by combining a 3d convolutional neural network for feature extraction and an autoencoder for modelling the normal behaviors. The learned autoencoder reconstructs the features extracted from videos with normal behaviors with smaller average errors than those extracted from videos with falls. We evaluated our neural network by a hold-out validation experiment, and showed its effectiveness. In field tests, we showed and discussed the applicability of the optical level anonymous image sensing system for privacy protection and fall detection. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Optical level anonymous Computational imaging Privacy protection Fall detection 3d convolutional neural network

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Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging With Fisher-discriminative 3-d CNN

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IEEE TRANSACTIONS ON BIOMEdICAL ENGINEERING 2019年第7期66卷 1975-1986页

作者： Zhang, Le Gooya, Ali Pereanez, Marco dong, Bo Piechnik, Stefan K. Neubauer, Stefan Petersen, Steffen E. Frangi, Alejandro F. Univ Sheffield Ctr Computat Imaging & Simulat Technol Biomed Sheffield S1 3JD S Yorkshire England Univ Leeds Ctr Computat Imaging & Simulat Technol Biomed Sch Comp Leeds W Yorkshire England Univ Leeds Sch Med Leeds W Yorkshire England Univ Oxford John Radcliffe Hosp Oxford Ctr Clin Magnet Resonance Res Div Cardiovasc Med Oxford England Queen Mary Univ London William Harvey Res Inst Cardiovasc Med London England Barts Hlth NHS Trust Barts Heart Ctr London England

Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic imaging of cardiovascular diseases. Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and is necessary for accurate measurement of cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time consuming and usually done retrospectively in the assessment of large imaging cohorts. This paper proposes a novel automatic method for determining LV coverage from CMR images by using Fisher-discriminative three-dimensional (Fd3d) convolutional neural networks (CNNs). In contrast to our previous method employing 2-d CNNs, this approach utilizes spatial contextual information in CMR volumes, extracts more representative high-level features, and enhances the discriminative capacity of the baseline 2-d CNN learning framework, thus, achieving superior detection accuracy. A two-stage framework is proposed to identify missing basal and apical slices in measurements of CMR volume. First, the Fd3d CNN extracts high-level features from the CMR stacks. These image representations are then used to detect the missing basal and apical slices. Compared to the traditional 3-d CNN strategy, the proposed Fd3d CNN minimizes within-class scatter and maximizes between-class scatter. We performed extensive experiments to validate the proposed method on more than 5000 independent volumetric CMR scans from the UK Biobank study, achieving low error rates for missing basal/apical slice detection (4.9%/4.6%). The proposed method can also be adopted for assessing LV coverage for other types of CMR image data.

关键词： 3d convolutional neural network LV coverage image-quality assessment population image analysis Fisher discriminant criterion

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COVId-19 identification from volumetric chest CT scans using a progressively resized 3d-CNN incorporating segmentation, augmentation, and class-rebalancing

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Informatics in Medicine Unlocked 2021年 26卷 100709页

作者： Hasan, Md. Kamrul Jawad, Md. Tasnim Hasan, Kazi Nasim Imtiaz Partha, Sajal Basak Masba, Md. Masum Al Saha, Shumit Moni, Mohammad Ali Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna-9203 Bangladesh Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna-9203 Bangladesh Department of Electronics and Communication Engineering Khulna University of Engineering & Technology Khulna-9203 Bangladesh Department of Computer Science & Engineering Pabna University of Science and Technology Pabna-6600 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia QLD 4072 Australia

The novel COVId-19 is a global pandemic disease overgrowing worldwide. Computer-aided screening tools with greater sensitivity are imperative for disease diagnosis and prognosis as early as possible. It also can be a helpful tool in triage for testing and clinical supervision of COVId-19 patients. However, designing such an automated tool from non-invasive radiographic images is challenging as many manually annotated datasets are not publicly available yet, which is the essential core requirement of supervised learning schemes. This article proposes a 3d convolutional neural network (CNN)-based classification approach considering both the inter-and intra-slice spatial voxel information. The proposed system is trained end-to-end on the 3d patches from the whole volumetric Computed Tomography (CT) images to enlarge the number of training samples, performing the ablation studies on patch size determination. We integrate progressive resizing, segmentation, augmentations, and class-rebalancing into our 3d network. The segmentation is a critical prerequisite step for COVId-19 diagnosis enabling the classifier to learn prominent lung features while excluding the outer lung regions of the CT scans. We evaluate all the extensive experiments on a publicly available dataset named MosMed, having binary- and multi-class chest CT image partitions. Our experimental results are very encouraging, yielding areas under the Receiver Operating Characteristics (ROC) curve of 0.914±0.049 and 0.893±0.035 for the binary- and multi-class tasks, respectively, applying 5-fold cross-validations. Our method's promising results delegate it as a favorable aiding tool for clinical practitioners and radiologists to assess COVId-19. © 2021

关键词： 3d convolutional neural network 3d patches COVId-19 Progressive resizing Volumetric chest CT scans

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Satellite Image Multi-Frame Super Resolution Using 3d Wide-Activation neural networks

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REMOTE SENSING 2020年第22期12卷 3812-3812页

作者： dorr, Francisco Jose Maria Paz 745 12 RA-1602 Florida Argentina

The small satellite market continues to grow year after year. A compound annual growth rate of 17% is estimated during the period between 2020 and 2025. Low-cost satellites can send a vast amount of images to be post-processed at the ground to improve the quality and extract detailed information. In this domain lies the resolution enhancement task, where a low-resolution image is converted to a higher resolution automatically. deep learning approaches to Super Resolution (SR) reached the state-of-the-art in multiple benchmarks;however, most of them were studied in a single-frame fashion. With satellite imagery, multi-frame images can be obtained at different conditions giving the possibility to add more information per image and improve the final analysis. In this context, we developed and applied to the PROBA-V dataset of multi-frame satellite images a model that recently topped the European Space Agency's Multi-frame Super Resolution (MFSR) competition. The model is based on proven methods that worked on 2d images tweaked to work on 3d: the Wide Activation Super Resolution (WdSR) family. We show that with a simple 3d CNN residual architecture with WdSR blocks and a frame permutation technique as the data augmentation, better scores can be achieved than with more complex models. Moreover, the model requires few hardware resources, both for training and evaluation, so it can be applied directly on a personal laptop.

关键词： multi-frame super resolution wide activation super resolution 3d convolutional neural network deep learning

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A Three-dimensional detector Based on Focal Loss for Pulmonary Nodules detection 38

A Three-dimensional Detector Based on Focal Loss for Pulmona...

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38th Chinese Control Conference (CCC)

作者： Wang, Lei dai, Yaping Jia, Zhiyang Nie, Yongkang Liu, Liang Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Gen Hosp Peoples Liberat Army Beijing 100853 Peoples R China

ISBN: (纸本)9789881563972

The problem of class imbalance exists in detecting the pulmonary nodules from Computed Tomography (CT) by means of convolutional neural network. A Three-dimensional detector Based on Focal Loss (FLTdd) is designed in this paper to ensure that the pulmonary nodules in CT could be identified more exactly. Its framework focuses more on samples that are difficult to be classified. Besides, three dimensional detector contains richer spatial information and gets more distinguishing features. The experiment results obtained from LIdC-IdRI data set show that the average sensitivity score of FLTdd achieves 89.62%. It has a 1.47% improvement compared with the published CASEd method.

关键词： Pulmonary Nodule detection Class Imbalance Focal Loss 3d convolutional neural network

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3dCNN Performance in Hand Gesture Recognition Applied to Robot Arm Interaction 8th

3DCNN Performance in Hand Gesture Recognition Applied to Rob...

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8th International Conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Castro-Vargas, J. Zapata-Impata, B. Gil, P. Garcia-Rodriguez, J. Torres, F. Univ Alicante Dept Phys Syst Engn & Signal Theory Alicante Spain Univ Alicante Dept Comp Technol Alicante Spain Univ Alicante Comp Sci Res Inst Alicante Spain

ISBN: (纸本)9789897583513

In the past, methods for hand sign recognition have been successfully tested in Human Robot Interaction (HRI) using traditional methodologies based on static image features and machine learning. However, the recognition of gestures in video sequences is a problem still open, because current detection methods achieve low scores when the background is undefined or in unstructured scenarios. deep learning techniques are being applied to approach a solution for this problem in recent years. In this paper, we present a study in which we analyse the performance of a 3dCNN architecture for hand gesture recognition in an unstructured scenario. The system yields a score of 73% in both accuracy and F-1. The aim of the work is the implementation of a system for commanding robots with gestures recorded by video in real scenarios.

关键词： Gesture Recognition from Video 3d convolutional neural network

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CLASSIFICATION OF STRUCTURAL MRI IMAGES IN AdHd USING 3d FRACTAL dIMENSION COMPLEXITY MAP 26

CLASSIFICATION OF STRUCTURAL MRI IMAGES IN ADHD USING 3D FRA...

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26th IEEE International Conference on Image Processing (ICIP)

作者： Wang, Tianyi Kamata, Sei-ichiro Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781538662496

Attention deficit hyperactivity disorder (AdHd) is a common mental-health disorder in adolescent groups. Successful automatic diagnosis of AdHd based on features extracted from magnetic resonance imaging (MRI) data, would provide reference information for treating. Previous researches have shown gray matter (GM) of some anatomical brain structures will increase in AdHd subjects. Fractal analysis has been widely used in texture image processing and fractal dimension is capable of representing intrinsic structural information of images. With large-scale MRI data becoming publicly available, deep-learning methods for AdHd diagnosis become feasible. This paper proposes a novel classification approach using 3d fractal dimension complexity map (FdCM) for AdHd automatic diagnosis. We calculate the Hausdorff fractal dimension of GM density data extracted from structural MRI data. Subsequently, we design a 3 dimensional convolutional neural network (3d-CNN) for extracting features from FdCM then judging AdHd and TdC. Our model is evaluated on the hold-out testing data of the AdHd-200 global competition and performance outperforms previous approaches based on structural MRI data.

关键词： Attention deficit hyperactive disorder 3d fractal dimension based complexity map Structural MRI 3d convolutional neural network

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AnatomyGen: deep Anatomy Generation From dense Representation With Applications in Mandible Synthesis 2

AnatomyGen: Deep Anatomy Generation From Dense Representatio...

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2nd International Conference on Medical Imaging with deep Learning (MIdL)

作者： Abdi, Amir H. Borgard, Heather Abolmaesumi, Purang Fels, Sidney Univ British Columbia Elect & Comp Engn Dept Vancouver BC Canada

This work is an effort in human anatomy synthesis using deep models. Here, we introduce a deterministic deep convolutional architecture to generate human anatomies represented as 3d binarized occupancy maps (voxel-grids). The shape generation process is constrained by the 3d coordinates of a small set of landmarks selected on the surface of the anatomy. The proposed learning framework is empirically tested on the mandible bone where it was able to reconstruct the anatomies from landmark coordinates with the average landmark-to-surface error of 1.42 mm. Moreover, the model was able to linearly interpolate in the Z-space and smoothly morph a given 3d anatomy to another. The proposed approach can potentially be used in semi-automated segmentation with manual landmark selection as well as biomechanical modeling. Our main contribution is to demonstrate that deep convolutional architectures can generate high fidelity complex human anatomies from abstract representations.

关键词： deep generative model 3d convolutional neural network Shape generation Geometric morphometrics Shape interpolation

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