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Deep convolutional neural network

Matthew J Nyflot, Phawis Thammasorn, Landon S Wootton, Eric C Ford, W Art Chaovalitwongse
PURPOSE: Patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) is a ubiquitous clinical procedure, but conventional methods have often been criticized as being insensitive to errors or less effective than other common physics checks. Recently, there has been interest in the application of radiomics, quantitative extraction of image features, to radiotherapy quality assurance. In this work we investigate a deep learning approach to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient-specific QA...
December 13, 2018: Medical Physics
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
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
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
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
Yanan Sun, Bing Xue, Mengjie Zhang, Gary G Yen
Convolutional autoencoders (CAEs) have shown their remarkable performance in stacking to deep convolutional neural networks (CNNs) for classifying image data during the past several years. However, they are unable to construct the state-of-the-art CNNs due to their intrinsic architectures. In this regard, we propose a flexible CAE (FCAE) by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional CAE. We also design an architecture discovery method by exploiting particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed FCAE with much less computational resource and without any manual intervention...
December 10, 2018: IEEE Transactions on Neural Networks and Learning Systems
Ha Tran Hong Phan, Ashnil Kumar, David Feng, Michael Fulham, Jinman Kim
Automatic event detection in cell videos is essential for monitoring cell populations in biomedicine. Deep learning methods have advantages over traditional approaches for cell event detection due to their ability to capture more discriminative features of cellular processes. Supervised deep learning methods however are inherently limited due to the scarcity of annotated data. Unsupervised deep learning methods have shown promise in general (non-cell) videos because they can learn the visual appearance and motion of regularly occurring events...
December 7, 2018: IEEE Transactions on Medical Imaging
Alexander Andonian, Daniel Paseltiner, Travis J Gould, Jason B Castro
BACKGROUND: The Allen Mouse Brain Atlas allows study of the brain's molecular anatomy at cellular scale, for thousands of. To fully leverage this resource, one must register histological images of brain tissue -- a task made challenging by the brain's structural complexity and heterogeneity, as well as inter-experiment variability. NEW METHOD: We have developed a deep-learning based methodology for classification and registration of thousands of sections of brain tissue, using the mouse olfactory bulb (OB) as a case study...
December 6, 2018: Journal of Neuroscience Methods
Xiaoyan Wei, Lin Zhou, Ziyi Chen, Liangjun Zhang, Yi Zhou
BACKGROUND: Automated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain experts, which are inflexible for the detection of a variety of patterns in a large amount of patients' EEG data. Moreover, conventional machine learning algorithms for seizure detection cannot accommodate multi-channel Electroencephalogram (EEG) data effectively, which contains both temporal and spatial information...
December 7, 2018: BMC Medical Informatics and Decision Making
Alexander D Weston, Panagiotis Korfiatis, Timothy L Kline, Kenneth A Philbrick, Petro Kostandy, Tomas Sakinis, Motokazu Sugimoto, Naoki Takahashi, Bradley J Erickson
Purpose To develop and evaluate a fully automated algorithm for segmenting the abdomen from CT to quantify body composition. Materials and Methods For this retrospective study, a convolutional neural network based on the U-Net architecture was trained to perform abdominal segmentation on a data set of 2430 two-dimensional CT examinations and was tested on 270 CT examinations. It was further tested on a separate data set of 2369 patients with hepatocellular carcinoma (HCC). CT examinations were performed between 1997 and 2015...
December 11, 2018: Radiology
Kevin T Chen, Enhao Gong, Fabiola Bezerra de Carvalho Macruz, Junshen Xu, Athanasia Boumis, Mehdi Khalighi, Kathleen L Poston, Sharon J Sha, Michael D Greicius, Elizabeth Mormino, John M Pauly, Shyam Srinivas, Greg Zaharchuk
Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age ± standard deviation [SD], 67 years ± 8), including 16 male patients and 23 female patients (mean age, 66 years ± 6 and 68 years ± 9, respectively), who underwent simultaneous amyloid (fluorine 18 [18 F]-florbetaben) PET/MRI examinations were acquired from March 2016 through October 2017 and retrospectively analyzed...
December 11, 2018: Radiology
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