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Deep convolutional neural network

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
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
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rarity, few methods have been developed for predicting cis-isomers from protein sequences, most of which are based on outdated datasets and lack the means for independent testing.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
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
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
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
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
Vasant P Kearney, Samuel Haaf, Atchar Sudhyadhom, Gilmer Valdes, Timothy D Solberg
The purpose of the work is to develop a deep unsupervised learning strategy for cone-beam CT (CBCT) to CT deformable image registration (DIR). This technique uses a deep convolutional inverse graphics network (DCIGN) based DIR algorithm implemented on 2 Nvidia 1080 Ti graphics processing units. The model is comprised of an encoding and decoding stage. The fully-convolutional encoding stage learns hierarchical features and simultaneously forms an information bottleneck, while the decoding stage restores the original dimensionality of the input image...
August 15, 2018: Physics in Medicine and Biology
Tomohiro Kajikawa, Noriyuki Kadoya, Kengo Ito, Yoshiki Takayama, Takahito Chiba, Seiji Tomori, Ken Takeda, Keiichi Jingu
The quality of radiotherapy has greatly improved due to the high precision achieved by intensity-modulated radiation therapy (IMRT). Studies have been conducted to increase the quality of planning and reduce the costs associated with planning through automated planning method; however, few studies have used the deep learning method for optimization of planning. The purpose of this study was to propose an automated method based on a convolutional neural network (CNN) for predicting the dosimetric eligibility of patients with prostate cancer undergoing IMRT...
August 14, 2018: Radiological Physics and Technology
Lingyun Song, Jun Liu, Buyue Qian, Mingxuan Sun, Kuan Yang, Meng Sun, Samar Abbas
Deep Convolutional Neural Networks (CNNs) have shown superior performance on the task of single-label image classification. However, the applicability of CNNs to multilabel images still remains an open problem, mainly because of two reasons. First, each image is usually treated as an inseparable entity and represented as one instance, which mixes the visual information corresponding to different labels. Second, the correlations amongst labels are often overlooked. To address these limitations, we propose a deep Multi-Modal CNN for Multi-Instance Multi-Label image classification, called MMCNNMIML...
August 10, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Hemant K Aggarwal, Merry P Mani, Mathews Jacob
We introduce a model-based image reconstruction framework with a convolution neural network (CNN) based regularization prior. The proposed formulation provides a systematic approach for deriving deep architectures for inverse problems with the arbitrary structure. Since the forward model is explicitly accounted for, a smaller network with fewer parameters is sufficient to capture the image information compared to direct inversion approaches, thus reducing the demand for training data and training time. Since we rely on end-to-end training with weight sharing across iterations, the CNN weights are customized to the forward model, thus offering improved performance over approaches that rely on pre-trained denoisers...
August 13, 2018: IEEE Transactions on Medical Imaging
Sarah E Gerard, Taylor J Patton, Gary E Christensen, John E Bayouth, Joseph M Reinhardt
Pulmonary fissure detection in computed tomography (CT) is a critical component for automatic lobar segmentation. The majority of fissure detection methods use feature descriptors that are hand-crafted, low-level, and have local spatial extent. The design of such feature detectors is typically targeted towards normal fissure anatomy, yielding low sensitivity to weak and abnormal fissures that are common in clinical datasets. Furthermore, local features commonly suffer from low specificity, as the complex textures in the lung can be indistinguishable from the fissure when global context is not considered...
August 10, 2018: IEEE Transactions on Medical Imaging
Chao Ma, Jia-Bin Huang, Xiaokang Yang, Ming-Hsuan Yang
Visual tracking is challenging as target objects often undergo significant appearance changes caused by deformation, abrupt motion, background clutter and occlusion. In this paper, we propose to exploit the rich hierarchical features of deep convolutional neural networks to improve the accuracy and robustness of visual tracking. Deep neural networks trained on object recognition datasets consist of multiple convolutional layers. These layers encode target appearance with different levels of abstraction. For example, the outputs of the last convolutional layers encode the semantic information of targets and such representations are invariant to significant appearance variations...
August 13, 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang
Convolutional neural networks have recently demonstrated high-quality reconstruction for single image super-resolution. However, existing methods often require a large number of network parameters and entail heavy computational loads at runtime for generating high-accuracy super-resolution results. In this paper, we propose the deep Laplacian Pyramid Super-Resolution Network for fast and accurate image super-resolution. The proposed network progressively reconstructs the sub-band residuals of high-resolution images at multiple pyramid levels...
August 13, 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Chi Li, M Zeeshan Zia, Quoc-Huy Tran, Xiang Yu, Gregory D Hager, Manmohan Chandraker
Recent data-driven approaches to scene interpretation predominantly pose inference as an end-to-end black-box mapping, commonly performed by a Convolutional Neural Network (CNN). However, decades of work on perceptual organization in both human and machine vision suggest that there are often intermediate representations that are intrinsic to an inference task, and which provide essential structure to improve generalization. In this work, we explore an approach for injecting prior domain structure into neural network training by supervising hidden layers of a CNN with intermediate concepts that normally are not observed in practice...
August 13, 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
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