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Convolutional Neural Networks

Yoshimasa Horie, Toshiyuki Yoshio, Kazuharu Aoyama, Syouichi Yoshimizu, Yusuke Horiuchi, Akiyoshi Ishiyama, Toshiaki Hirasawa, Tomohiro Tuchida, Tsuyoshi Ozawa, Soichiro Ishihara, Youichi Kumagai, Mitsuhiro Fujishiro, Iruru Maetani, Junko Fujisaki, Tomohiro Tada
BACKGROUND AND AIMS: The prognosis of esophageal cancer is relatively poor. Patients are usually diagnosed at an advanced stage when it is often too late for effective treatment. Recently, artificial intelligence (AI) using deep learning has made remarkable progress in medicine. However, there are no reports on its application for diagnosing esophageal cancer. Here, we demonstrate the diagnostic ability of AI to detect esophageal cancer including squamous cell carcinoma and adenocarcinoma...
August 15, 2018: Gastrointestinal Endoscopy
Julie Chang, Vincent Sitzmann, Xiong Dun, Wolfgang Heidrich, Gordon Wetzstein
Convolutional neural networks (CNNs) excel in a wide variety of computer vision applications, but their high performance also comes at a high computational cost. Despite efforts to increase efficiency both algorithmically and with specialized hardware, it remains difficult to deploy CNNs in embedded systems due to tight power budgets. Here we explore a complementary strategy that incorporates a layer of optical computing prior to electronic computing, improving performance on image classification tasks while adding minimal electronic computational cost or processing time...
August 17, 2018: Scientific Reports
Zhenwei Zhang, James L Coyle, Ervin Sejdić
The displacement of the hyoid bone is one of the key components evaluated in the swallow study, as its motion during swallowing is related to overall swallowing integrity. In daily research settings, experts visually detect the hyoid bone in the video frames and manually plot hyoid bone position frame by frame. This study aims to develop an automatic method to localize the location of the hyoid bone in the video sequence. To automatically detect the location of the hyoid bone in a frame, we proposed a single shot multibox detector, a deep convolutional neural network, which is employed to detect and classify the location of the hyoid bone...
August 17, 2018: Scientific Reports
Venkata Joopudi, Bharath Dandala, Murthy Devarakonda
OBJECTIVE: Abbreviations sense disambiguation is a special case of word sense disambiguation. Machine learning methods based on neural networks showed promising results for word sense disambiguation [1] and, here we assess their effectiveness for abbreviation sense disambiguation METHODS: Convolutional Neural Network (CNN) models were trained, one for each abbreviation, to disambiguate abbreviation senses. A reverse substitution (of long forms with short forms) method from a previous study was used on clinical narratives from Cleveland Clinic, USA, to auto-generate training data...
August 14, 2018: Journal of Biomedical Informatics
Jaswinder Singh, Jack Hanson, Rhys Heffernan, Kuldip Paliwal, Yuedong Yang, Yaoqi Zhou
It has been long established that cis conformations of amino acid residues play many biologically important roles despite their rare occurrence in protein structure. Due to this rarity, few methods have been developed for predicting cis-isomers from protein sequences, most of which are based on outdated datasets and lack the means for independent testing. In this work, using a database of >10000 high-resolution protein structures, we update the statistics of cis-isomers and develop a sequence-based prediction technique using an ensemble of residual convolutional and Long Short-Term Memory bidirectional recurrent neural networks which allow learning from the whole protein sequence...
August 17, 2018: Journal of Chemical Information and Modeling
Zewei He, Yanpeng Cao, Yafei Dong, Jiangxin Yang, Yanlong Cao, Christel-Löic Tisse
Fixed-pattern noise (FPN), which is caused by the nonuniform opto-electronic responses of microbolometer focal-plane-array (FPA) optoelectronics, imposes a challenging problem in infrared imaging systems. In this paper, we successfully demonstrate that a better single-image-based non-uniformity correction (NUC) operator can be directly learned from a large number of simulated training images instead of being handcrafted as before. Our proposed training scheme, which is based on convolutional neural networks (CNNs) and a column FPN simulation module, gives rise to a powerful technique to reconstruct the noise-free infrared image from its corresponding noisy observation...
June 20, 2018: Applied Optics
Yujin Chen, Ruizhi Chen, Mengyun Liu, Aoran Xiao, Dewen Wu, Shuheng Zhao
Indoor localization is one of the fundamentals of location-based services (LBS) such as seamless indoor and outdoor navigation, location-based precision marketing, spatial cognition of robotics, etc. Visual features take up a dominant part of the information that helps human and robotics understand the environment, and many visual localization systems have been proposed. However, the problem of indoor visual localization has not been well settled due to the tough trade-off of accuracy and cost. To better address this problem, a localization method based on image retrieval is proposed in this paper, which mainly consists of two parts...
August 16, 2018: Sensors
Shay Elmalem, Raja Giryes, Emanuel Marom
Modern consumer electronics market dictates the need for small-scale and high-performance cameras. Such designs involve trade-offs between various system parameters. In such trade-offs, Depth Of Field (DOF) is a significant issue very often. We propose a computational imaging-based technique to overcome DOF limitations. Our approach is based on the synergy between a simple phase aperture coding element and a convolutional neural network (CNN). The phase element, designed for DOF extension using color diversity in the imaging system response, causes chromatic variations by creating a different defocus blur for each color channel of the image...
June 11, 2018: Optics Express
Mark D Hannel, Aidan Abdulali, Michael O'Brien, David G Grier
Holograms of colloidal particles can be analyzed with the Lorenz-Mie theory of light scattering to measure individual particles' three-dimensional positions with nanometer precision while simultaneously estimating their sizes and refractive indexes. Extracting this wealth of information begins by detecting and localizing features of interest within individual holograms. Conventionally approached with heuristic algorithms, this image analysis problem can be solved faster and more generally with machine-learning techniques...
June 11, 2018: Optics Express
Jiri Chmelik, Roman Jakubicek, Petr Walek, Jiri Jan, Petr Ourednicek, Lukas Lambert, Elena Amadori, Giampaolo Gavelli
This paper aims to address the segmentation and classification of lytic and sclerotic metastatic lesions that are difficult to define by using spinal 3D Computed Tomography (CT) images obtained from highly pathologically affected cases. As the lesions are ill-defined and consequently it is difficult to find relevant image features that would enable detection and classification of lesions by classical methods of texture and shape analysis, the problem is solved by automatic feature extraction provided by a deep Convolutional Neural Network (CNN)...
August 3, 2018: Medical Image Analysis
Gong Zhang, Tian Guan, Zhiyuan Shen, Xiangnan Wang, Tao Hu, Delai Wang, Yonghong He, Ni Xie
Traditional digital holographic imaging algorithms need multiple iterations to obtain focused reconstructed image, which is time-consuming. In terms of phase retrieval, there is also the problem of phase compensation in addition to focusing task. Here, a new method is proposed for fast digital focus, where we use U-type convolutional neural network (U-net) to recover the original phase of microscopic samples. Generated data sets are used to simulate different degrees of defocused image, and verify that the U-net can restore the original phase to a great extent and realize phase compensation at the same time...
July 23, 2018: Optics Express
Junfeng Zhang, Wei Chen, Mingyi Gao, Yuanyuan Ma, Yongli Zhao, Wei Chen, Gangxiang Shen
In a cognitive, heterogeneous, optical network, it would be important to identify physical layer information, especially the modulation formats of transmitted signals. The modulation format information is also indispensable for carrier-phase-recovery in a coherent optical receiver. Because constellation diagrams of modulation signals are susceptible to various noises, we utilize a convolutional neural network to process the amplitude data after the modulation-format-agnostic clock recovery. Furthermore, for the carrier-phase-recovered data, we use the clustering method based on a fast search and find the density peaks to classify the constellation clusters and use the k-nearest-neighbor method to label the samples...
July 9, 2018: Optics Express
Wenqi Wu, Yingjie Yin, Xingang Wang, De Xu
In recent years, the application of deep learning based on deep convolutional neural networks has gained great success in face detection. However, one of the remaining open challenges is the detection of small-scaled faces. The depth of the convolutional network can cause the projected feature map for small faces to be quickly shrunk, and most detection approaches with scale invariant can hardly handle less than 15$x$15 pixel faces. To solve this problem, we propose a different scales face detector (DSFD) based on Faster R-CNN...
August 14, 2018: IEEE Transactions on Cybernetics
Clement Zotti, Zhiming Luo, Alain Lalande, Pierre-Marc Jodoin
In this paper, we present a novel convolutional neural network (CNN) architecture to segment images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed model is an extension of the U-net that embeds a cardiac shape prior and involves a loss function tailored to the cardiac anatomy. Our system takes as input raw MR images, requires no manual preprocessing or image cropping and is trained to segment the endocardium and epicardium of the left ventricle, the endocardium of the right ventricle, as well as the center of the left ventricle...
August 14, 2018: IEEE Journal of Biomedical and Health Informatics
Huan Yang, Baoyuan Wang, Noranart Vesdapunt, Minyi Guo, Sing Bing Kang
We propose a reinforcement learning approach for real-time exposure control of a mobile camera that is personalizable. Our approach is based on Markov Decision Process (MDP). In the camera viewfinder or live preview mode, given the current frame, our system predicts the change in exposure so as to optimize the trade-off among image quality, fast convergence, and minimal temporal oscillation. We model the exposure prediction function as a fully convolutional neural network that can be trained through Gaussian policy gradient in an end-to-end fashion...
August 15, 2018: IEEE Transactions on Visualization and Computer Graphics
Zixia Zhou, Yuanyuan Wang, Jinhua Yu, Yi Guo, Wei Guoand, Yanxing Qi
In recent years, plane-wave imaging (PWI) has attracted considerable attention because of its high temporal resolution. However, the low spatial resolution of PWI limits its clinical applications, which has inspired various studies on the high spatial resolution reconstruction of plane-wave (PW) ultrasound images. Although compounding methods and traditional high spatial resolution reconstruction approaches can improve the image quality, these techniques decrease the temporal resolution. Since learning methods can fully reserve the high temporal resolution of PW ultrasounds, a novel convolutional neural network (CNN) model for the high spatial-temporal resolution reconstruction of PW ultrasound images is proposed in this paper...
August 14, 2018: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Faisal Mahmood, Richard Chen, Sandra Sudarsky, Daphne Yu, Nicholas J Durr
Deep learning has emerged as a powerful artificial intelligence tool to interpret medical images for a growing variety of applications. However, the paucity of medical imaging data with high-quality annotations that is necessary for training such methods ultimately limits their performance. Medical data is challenging to acquire due to privacy issues, shortage of experts available for annotation, limited representation of rare conditions and cost. This problem has previously been addressed by using synthetically generated data...
August 16, 2018: Physics in Medicine and Biology
Xiaoqing Wang, Xiangjun Wang, Yubo Ni
In the facial expression recognition task, a good-performing convolutional neural network (CNN) model trained on one dataset (source dataset) usually performs poorly on another dataset (target dataset). This is because the feature distribution of the same emotion varies in different datasets. To improve the cross-dataset accuracy of the CNN model, we introduce an unsupervised domain adaptation method, which is especially suitable for unlabelled small target dataset. In order to solve the problem of lack of samples from the target dataset, we train a generative adversarial network (GAN) on the target dataset and use the GAN generated samples to fine-tune the model pretrained on the source dataset...
2018: Computational Intelligence and Neuroscience
Lamyaa Sadouk, Taoufiq Gadi, El Hassan Essoufi
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent difficulties including repetitive patterns of behavior known as stereotypical motor movements (SMM). So far, several techniques have been implemented to track and identify SMMs. In this context, we propose a deep learning approach for SMM recognition, namely, convolutional neural networks (CNN) in time and frequency-domains. To solve the intrasubject SMM variability, we propose a robust CNN model for SMM detection within subjects, whose parameters are set according to a proper analysis of SMM signals, thereby outperforming state-of-the-art SMM classification works...
2018: Computational Intelligence and Neuroscience
Luigi Celona, Simone Bianco, Raimondo Schettini
We present a multi-task learning-based convolutional neural network (MTL-CNN) able to estimate multiple tags describing face images simultaneously. In total, the model is able to estimate up to 74 different face attributes belonging to three distinct recognition tasks: age group, gender and visual attributes (such as hair color, face shape and the presence of makeup). The proposed model shares all the CNN's parameters among tasks and deals with task-specific estimation through the introduction of two components: (i) a gating mechanism to control activations' sharing and to adaptively route them across different face attributes; (ii) a module to post-process the predictions in order to take into account the correlation among face attributes...
August 14, 2018: Sensors
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