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Convolutional Neural Networks

Youichi Kumagai, Kaiyo Takubo, Kenro Kawada, Kazuharu Aoyama, Yuma Endo, Tsuyoshi Ozawa, Toshiaki Hirasawa, Toshiyuki Yoshio, Soichiro Ishihara, Mitsuhiro Fujishiro, Jun-Ichi Tamaru, Erito Mochiki, Hideyuki Ishida, Tomohiro Tada
BACKGROUND AND AIMS: The endocytoscopic system (ECS) helps in virtual realization of histology and can aid in confirming histological diagnosis in vivo. We propose replacing biopsy-based histology for esophageal squamous cell carcinoma (ESCC) by using the ECS. We applied deep-learning artificial intelligence (AI) to analyse ECS images of the esophagus to determine whether AI can support endoscopists for the replacement of biopsy-based histology. METHODS: A convolutional neural network-based AI was constructed based on GoogLeNet and trained using 4715 ECS images of the esophagus (1141 malignant and 3574 non-malignant images)...
December 13, 2018: Esophagus: Official Journal of the Japan Esophageal Society
Magdalena Ola Cichocka, Jonas Ångström, Bin Wang, Xiaodong Zou, Stef Smeets
Single-crystal electron diffraction (SCED) is emerging as an effective technique to determine and refine the structures of unknown nano-sized crystals. In this work, the implementation of the continuous rotation electron diffraction (cRED) method for high-throughput data collection is described. This is achieved through dedicated software that controls the transmission electron microscope and the camera. Crystal tracking can be performed by defocusing every n th diffraction pattern while the crystal rotates, which addresses the problem of the crystal moving out of view of the selected area aperture during rotation...
December 1, 2018: Journal of Applied Crystallography
Tianyuan Liu, Jinsong Bao, Junliang Wang, Yiming Zhang
At present, realizing high-quality automatic welding through online monitoring is a research focus in engineering applications. In this paper, a CNN⁻LSTM algorithm is proposed, which combines the advantages of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The CNN⁻LSTM algorithm establishes a shallow CNN to extract the primary features of the molten pool image. Then the feature tensor extracted by the CNN is transformed into the feature matrix. Finally, the rows of the feature matrix are fed into the LSTM network for feature fusion...
December 10, 2018: Sensors
Ahsiah Ismail, Mohd Yamani Idna Idris, Mohamad Nizam Ayub, Lip Yee Por
Smart manufacturing enables an efficient manufacturing process by optimizing production and product transaction. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods are studied to determine the best vision-based classification technique, namely Bag of Words (BOW), Spatial Pyramid Matching (SPM) and Convolutional Neural Network (CNN)...
December 10, 2018: Sensors
Poliyapram Vinayaraj, Nevrez Imamoglu, Ryosuke Nakamura, Atsushi Oda
Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth's surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images...
December 7, 2018: Sensors
Dor Oppenheim, Guy Shani, Orly Erlich, Leah Tsror
Many plant diseases have distinct visual symptoms which can be used to identify and classify them correctly. This paper presents a potato disease classification algorithm which leverages these distinct appearances and the recent advances in computer vision made possible by deep learning. The algorithm uses a deep convolutional neural network training it to classify the tubers into five classes, namely, four disease classes and a healthy potato class. The database of images used in this study, containing potato tubers of different cultivars, sizes and diseases, was acquired, classified, and labeled manually by experts...
December 13, 2018: Phytopathology
Xiaolong Zheng, Peng Zheng, Rui-Zhi Zhang
In recent years, convolutional neural networks (CNNs) have achieved great success in image recognition and shown powerful feature extraction ability. Here we show that CNNs can learn the inner structure and chemical information in the periodic table. Using the periodic table as representation, and full-Heusler compounds in the Open Quantum Materials Database (OQMD) as training and test samples, a multi-task CNN was trained to output the lattice parameter and enthalpy of formation simultaneously. The mean prediction errors were within DFT precision, and the results were much better than those obtained using only Mendeleev numbers or a random-element-positioning table, indicating that the two-dimensional inner structure of the periodic table was learned by the CNN as useful chemical information...
November 28, 2018: Chemical Science
Huan Liu, Shu Zhang, Xi Jiang, Tuo Zhang, Heng Huang, Fangfei Ge, Lin Zhao, Xiao Li, Xintao Hu, Junwei Han, Lei Guo, Tianming Liu
The human cerebral cortex is highly folded into diverse gyri and sulci. Accumulating evidences suggest that gyri and sulci exhibit anatomical, morphological, and connectional differences. Inspired by these evidences, we performed a series of experiments to explore the frequency-specific differences between gyral and sulcal neural activities from resting-state and task-based functional magnetic resonance imaging (fMRI) data. Specifically, we designed a convolutional neural network (CNN) based classifier, which can differentiate gyral and sulcal fMRI signals with reasonable accuracies...
December 12, 2018: Cerebral Cortex
Kunhua Liu, Peisi Zhong, Yi Zheng, Kaige Yang, Mei Liu
Attention maps have been fused in the VggNet structure (EAC-Net) [1] and have shown significant improvement compared to that of the VggNet structure. However, in [1], E-Net was designed based on the facial action unit (AU) center and for facial AU detection only. Thus, for the use of attention maps in every image type, this paper proposed a new convolutional neural network (CNN) structure, P_VggNet, comprising the following parts: P_Net and VggNet with 16 layers (VggNet-16). The generation approach of P_Net was designed, and the P_VggNet structure was proposed...
2018: PloS One
Hiram Shaish, Simukayi Mutasa, Jasnit Makkar, Peter Chang, Lawrence Schwartz, Firas Ahmed
OBJECTIVE: The purpose of this study is to determine whether a convolutional neural network (CNN) can predict the maximum standardized uptake value (SUVmax ) of lymph nodes in patients with cancer using the unenhanced CT images from a PET/CT examination, thus providing a proof of concept for potentially using deep learning to diagnose nodal involvement. MATERIALS AND METHODS: Consecutive initial staging PET/CT scans obtained in 2017 for patients with pathologically proven malignancy were collected...
December 12, 2018: AJR. American Journal of Roentgenology
Makoto Murata, Yoshiko Ariji, Yasufumi Ohashi, Taisuke Kawai, Motoki Fukuda, Takuma Funakoshi, Yoshitaka Kise, Michihito Nozawa, Akitoshi Katsumata, Hiroshi Fujita, Eiichiro Ariji
OBJECTIVES: To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance. METHODS: Training data for 400 healthy and 400 inflamed maxillary sinuses were enhanced to 6000 samples in each category by data augmentation. Image patches were input into a deep-learning system, the learning process was repeated for 200 epochs, and a learning model was created. Newly-prepared testing image patches from 60 healthy and 60 inflamed sinuses were input into the learning model, and the diagnostic performance was calculated...
December 11, 2018: Oral Radiology
Fang Liu
PURPOSE: To describe and evaluate a segmentation method using joint adversarial and segmentation convolutional neural network to achieve accurate segmentation using unannotated MR image datasets. THEORY AND METHODS: A segmentation pipeline was built using joint adversarial and segmentation network. A convolutional neural network technique called cycle-consistent generative adversarial network (CycleGAN) was applied as the core of the method to perform unpaired image-to-image translation between different MR image datasets...
December 10, 2018: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
L Oakden-Rayner
No abstract text is available yet for this article.
December 10, 2018: Annals of Oncology: Official Journal of the European Society for Medical Oncology
H A Haenssle, C Fink, L Uhlmann
No abstract text is available yet for this article.
December 10, 2018: Annals of Oncology: Official Journal of the European Society for Medical Oncology
Jack Hanson, Kuldip Paliwal, Thomas Litfin, Yuedong Yang, Yaoqi Zhou
Motivation: Sequence-based prediction of one dimensional structural properties of proteins has been a long-standing subproblem of protein structure prediction. Recently, prediction accuracy has been significantly improved due to the rapid expansion of protein sequence and structure libraries and advances in deep learning techniques, such as residual convolutional networks (ResNets) and Long-Short-Term Memory Cells in Bidirectional Recurrent Neural Networks (LSTM-BRNNs). Here we leverage an ensemble of LSTM-BRNN and ResNet models, together with predicted residue-residue contact maps, to continue the push towards the attainable limit of prediction for 3- and 8-state secondary structure, backbone angles (θ, τ, ϕ, and ψ), half-sphere exposure, contact numbers, and solvent accessible surface area (ASA)...
December 7, 2018: Bioinformatics
Christian Lucas, André Kemmling, Nassim Bouteldja, Linda F Aulmann, Amir Madany Mamlouk, Mattias P Heinrich
Cerebrovascular diseases, in particular ischemic stroke, are one of the leading global causes of death in developed countries. Perfusion CT and/or MRI are ideal imaging modalities for characterizing affected ischemic tissue in the hyper-acute phase. If infarct growth over time could be predicted accurately from functional acute imaging protocols together with advanced machine-learning based image analysis, the expected benefits of treatment options could be better weighted against potential risks. The quality of the outcome prediction by convolutional neural networks (CNNs) is so far limited, which indicates that even highly complex deep learning algorithms are not fully capable of directly learning physiological principles of tissue salvation through weak supervision due to a lack of data (e...
2018: Frontiers in Neurology
Bineng Zhong, Bing Bai, Jun Li, Yulun Zhang, Yun Fu
A class-agnostic tracker typically consists of three key components, i.e., its motion model, its target appearance model, and its updating strategy. However, most recent topperforming trackers mainly focus on constructing complicated appearance models and updating strategies, while using comparatively simple and heuristic motion models that may result in an inefficient search and degrade the tracking performance. To address this issue, we propose a hierarchical tracker that learns to move and track based on the combination of data-driven search at the coarse level, and coarse-to-fine verification at the fine level...
December 5, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Henry Wing Fung Yeung, Junhui Hou, Xiaoming Chen, Jie Chen, Zhibo Chen, Yuk Ying Chung
Light field (LF) photography is an emerging paradigm for capturing more immersive representations of the real-world. However, arising from the inherent trade-off between the angular and spatial dimensions, the spatial resolution of LF images captured by commercial micro-lens based LF cameras are significantly constrained. In this paper, we propose effective and efficient end-to-end convolutional neural network models for spatially super-resolving LF images. Specifically, the proposed models have an hourglass shape, which allows feature extraction to be performed at the low resolution level to save both computational and memory costs...
December 5, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Yuming Fang, Guanqun Ding, Jia Li, Zhijun Fang
Stereoscopic saliency detection plays an important role in various stereoscopic video processing applications. However, conventional stereoscopic video saliency detection methods mainly use independent low-level features instead of extracting them automatically, and thus, they ignore the intrinsic relationship between the spatial and temporal information. In this paper, we propose a novel stereoscopic video saliency detection method based on 3D convolutional neural networks, namely Deep 3D Video Saliency (Deep3DSaliency)...
December 5, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Nisha Ramesh, Tolga Tasdizen
Even though convolutional neural networks (CNN) have been used for cell segmentation, they require pixel-level ground truth annotations. This paper proposes a multi-task learning algorithm for cell detection and segmentation using CNNs. We use dot annotations placed inside each cell indicating approximate cell centroids to create training datasets for the detection and segmentation tasks. The segmentation task is used to map the input image to foreground vs background regions, whereas, the detection task is to used to predict the centroids of the cells...
December 7, 2018: IEEE Journal of Biomedical and Health Informatics
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