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https://www.readbyqxmd.com/read/28926765/texture-and-art-with-deep-neural-networks
#1
REVIEW
Leon A Gatys, Alexander S Ecker, Matthias Bethge
Although the study of biological vision and computer vision attempt to understand powerful visual information processing from different angles, they have a long history of informing each other. Recent advances in texture synthesis that were motivated by visual neuroscience have led to a substantial advance in image synthesis and manipulation in computer vision using convolutional neural networks (CNNs). Here, we review these recent advances and discuss how they can in turn inspire new research in visual perception and computational neuroscience...
September 16, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28926232/prediction-errors-of-molecular-machine-learning-models-lower-than-hybrid-dft-error
#2
Felix A Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S Schoenholz, George E Dahl, Oriol Vinyals, Steven Kearnes, Patrick F Riley, O Anatole von Lilienfeld
We investigate the impact of choosing regres- sors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules. The performance of each regressor/representation/property combination is assessed using learning curves which report out- of-sample errors as a function of training set size with up to ∼118k distinct molecules. Molecular structures and properties at hybrid density functional theory (DFT) level of theory come from the QM9 database [Ramakrishnan et al, Scientific Data 1 140022 (2014)] and include enthalpies and free energies of atomization , HOMO/LUMO energies and gap, dipole moment, polarizability, zero point vibrational energy, heat capacity and the highest fundamental vibrational frequency...
September 19, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/28924576/deep-learning-in-breast-cancer-risk-assessment-evaluation-of-convolutional-neural-networks-on-a-clinical-dataset-of-full-field-digital-mammograms
#3
Hui Li, Maryellen L Giger, Benjamin Q Huynh, Natalia O Antropova
To evaluate deep learning in the assessment of breast cancer risk in which convolutional neural networks (CNNs) with transfer learning are used to extract parenchymal characteristics directly from full-field digital mammographic (FFDM) images instead of using computerized radiographic texture analysis (RTA), 456 clinical FFDM cases were included: a "high-risk" BRCA1/2 gene-mutation carriers dataset (53 cases), a "high-risk" unilateral cancer patients dataset (75 cases), and a "low-risk dataset" (328 cases)...
October 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28924568/convolutional-neural-network-for-high-accuracy-functional-near-infrared-spectroscopy-in-a-brain-computer-interface-three-class-classification-of-rest-right-and-left-hand-motor-execution
#4
Thanawin Trakoolwilaiwan, Bahareh Behboodi, Jaeseok Lee, Kyungsoo Kim, Ji-Woong Choi
The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI...
January 2018: Neurophotonics
https://www.readbyqxmd.com/read/28922128/deep-manifold-learning-combined-with-convolutional-neural-networks-for-action-recognition
#5
Xin Chen, Jian Weng, Wei Lu, Jiaming Xu, Jiasi Weng
Learning deep representations have been applied in action recognition widely. However, there have been a few investigations on how to utilize the structural manifold information among different action videos to enhance the recognition accuracy and efficiency. In this paper, we propose to incorporate the manifold of training samples into deep learning, which is defined as deep manifold learning (DML). The proposed DML framework can be adapted to most existing deep networks to learn more discriminative features for action recognition...
September 15, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28922121/motion-blur-kernel-estimation-via-deep-learning
#6
Xiangyu Xu, Jinshan Pan, Yu-Jin Zhang, Ming-Hsuan Yang
The success of the state-of-the-art deblurring methods mainly depends on restoration of sharp edges in a coarse-tofine kernel estimation process. In this paper, we propose to learn a deep convolutional neural network for extracting sharp edges from blurred images. Motivated by the success of the existing filtering based deblurring methods, the proposed model consists of two stages: suppressing extraneous details and enhancing sharp edges. We show that the two-stage model simplifies the learning process and effectively restores sharp edges...
September 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28922114/cross-modal-scene-networks
#7
Yusuf Aytar, Lluis Castrejon, Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we introduce a new cross-modal scene dataset. While convolutional neural networks can categorize scenes well, they also learn an intermediate representation not aligned across modalities, which is undesirable for cross-modal transfer applications. We present methods to regularize cross-modal convolutional neural networks so that they have a shared representation that is agnostic of the modality...
September 18, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28920900/fundamental-principles-on-learning-new-features-for-effective-dense-matching
#8
Feihu Zhang, Benjamin W Wah
In dense matching (including stereo matching and optical flow), nearly all existing approaches are based on simple features, such as gray or RGB color, gradient or simple transformations like census, to calculate matching costs. These features do not perform well in complex scenes that may involve radiometric changes, noises, overexposure and/or textureless regions. Various problems may appear, such as wrong matching at the pixel or region level, flattening/breaking of edges and/or even entire structural collapse...
September 14, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28920094/convolutional-auto-encoder-for-image-denoising-of-ultra-low-dose-ct
#9
Mizuho Nishio, Chihiro Nagashima, Saori Hirabayashi, Akinori Ohnishi, Kaori Sasaki, Tomoyuki Sagawa, Masayuki Hamada, Tatsuo Yamashita
OBJECTIVES: The purpose of this study was to validate a patch-based image denoising method for ultra-low-dose CT images. Neural network with convolutional auto-encoder and pairs of standard-dose CT and ultra-low-dose CT image patches were used for image denoising. The performance of the proposed method was measured by using a chest phantom. MATERIALS AND METHODS: Standard-dose and ultra-low-dose CT images of the chest phantom were acquired. The tube currents for standard-dose and ultra-low-dose CT were 300 and 10 mA, respectively...
August 2017: Heliyon
https://www.readbyqxmd.com/read/28917120/automated-arteriole-and-venule-classification-using-deep-learning-for-retinal-images-from-the-uk-biobank-cohort
#10
R A Welikala, P J Foster, P H Whincup, A R Rudnicka, C G Owen, D P Strachan, S A Barman
The morphometric characteristics of the retinal vasculature are associated with future risk of many systemic and vascular diseases. However, analysis of data from large population based studies is needed to help resolve uncertainties in some of these associations. This requires automated systems that extract quantitative measures of vessel morphology from large numbers of retinal images. Associations between retinal vessel morphology and disease precursors/outcomes may be similar or opposing for arterioles and venules...
September 8, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28914611/deep-convolutional-neural-network-with-transfer-learning-for-rectum-toxicity-prediction-in-cervical-cancer-radiotherapy-a-feasibility-study
#11
Xin Zhen, Jiawei Chen, Zichun Zhong, Brian Andrew Hrycushko, Linghong Zhou, Steve B Jiang, Kevin Albuquerque, Xuejun Gu
Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients...
September 15, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/28910352/deep-learning-approach-to-bacterial-colony-classification
#12
Bartosz Zieliński, Anna Plichta, Krzysztof Misztal, Przemysław Spurek, Monika Brzychczy-Włoch, Dorota Ochońska
In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria...
2017: PloS One
https://www.readbyqxmd.com/read/28894460/an-automatic-gastrointestinal-polyp-detection-system-in-video-endoscopy-using-fusion-of-color-wavelet-and-convolutional-neural-network-features
#13
Mustain Billah, Sajjad Waheed, Mohammad Motiur Rahman
Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to...
2017: International Journal of Biomedical Imaging
https://www.readbyqxmd.com/read/28892454/disease-staging-and-prognosis-in-smokers-using-deep-learning-in-chest-computed-tomography
#14
Germán González, Samuel Y Ash, Gonzalo Vegas Sanchez-Ferrero, Jorge Onieva Onieva, Farbod N Rahaghi, James C Ross, Alejandro Díaz, Raúl San José Estépar, George R Washko
RATIONALE: Deep learning is a powerful tool that may allow for improved outcome prediction. OBJECTIVES: To determine if deep learning, specifically convolutional neural network (CNN) analysis, could detect and stage chronic obstructive pulmonary disease (COPD) and predict acute respiratory disease events (ARD) and mortality in smokers. METHODS: A CNN was trained using CT scans from 7,983 COPDGene participants and evaluated using 1000 non-overlapping COPDGene participants and 1,672 ECLIPSE participants...
September 11, 2017: American Journal of Respiratory and Critical Care Medicine
https://www.readbyqxmd.com/read/28891512/decoding-of-visual-activity-patterns-from-fmri-responses-using-multivariate-pattern-analyses-and-convolutional-neural-network
#15
Raheel Zafar, Nidal Kamel, Mohamad Naufal, Aamir Saeed Malik, Sarat C Dass, Rana Fayyaz Ahmad, Jafri M Abdullah, Faruque Reza
Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set...
2017: Journal of Integrative Neuroscience
https://www.readbyqxmd.com/read/28885833/the-face-of-a-molecule
#16
William H Gerwick
Recent technological advances in mass spectrometry and NMR spectroscopy have enabled new approaches for the rapid and insightful profiling of natural product mixtures. MALDI-MS with the provision of biosynthetic heavy-isotope-labeled precursors can be a powerful method by which to interrogate a natural product metabolome and to gain insight into its unique constituents; this is illustrated herein by the detection, isolation, and characterization of cryptomaldamide. MS/MS-based Molecular Networks, facilitated by the Global Natural Products Social (GNPS) platform, is rapidly changing the way in which we dereplicate known natural products in mixtures, find new analogues in desired structure classes, and identify fundamentally new chemical entities...
September 8, 2017: Journal of Natural Products
https://www.readbyqxmd.com/read/28885144/trajectory-predictor-by-using-recurrent-neural-networks-in-visual-tracking
#17
Lituan Wang, Lei Zhang, Zhang Yi
Motion models have been proved to be a crucial part in the visual tracking process. In recent trackers, particle filter and sliding windows-based motion models have been widely used. Treating motion models as a sequence prediction problem, we can estimate the motion of objects using their trajectories. Moreover, it is possible to transfer the learned knowledge from annotated trajectories to new objects. Inspired by recent advance in deep learning for visual feature extraction and sequence prediction, we propose a trajectory predictor to learn prior knowledge from annotated trajectories and transfer it to predict the motion of target objects...
October 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28884120/automated-classification-of-lung-cancer-types-from-cytological-images-using-deep-convolutional-neural-networks
#18
Atsushi Teramoto, Tetsuya Tsukamoto, Yuka Kiriyama, Hiroshi Fujita
Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28883201/deep-learning-for-magnetic-resonance-fingerprinting-a-new-approach-for-predicting-quantitative-parameter-values-from-time-series
#19
Elisabeth Hoppe, Gregor Körzdörfer, Tobias Würfl, Jens Wetzl, Felix Lugauer, Josef Pfeuffer, Andreas Maier
The purpose of this work is to evaluate methods from deep learning for application to Magnetic Resonance Fingerprinting (MRF). MRF is a recently proposed measurement technique for generating quantitative parameter maps. In MRF a non-steady state signal is generated by a pseudo-random excitation pattern. A comparison of the measured signal in each voxel with the physical model yields quantitative parameter maps. Currently, the comparison is done by matching a dictionary of simulated signals to the acquired signals...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28881977/denoising-genome-wide-histone-chip-seq-with-convolutional-neural-networks
#20
Pang Wei Koh, Emma Pierson, Anshul Kundaje
Motivation: Chromatin immune-precipitation sequencing (ChIP-seq) experiments are commonly used to obtain genome-wide profiles of histone modifications associated with different types of functional genomic elements. However, the quality of histone ChIP-seq data is affected by many experimental parameters such as the amount of input DNA, antibody specificity, ChIP enrichment and sequencing depth. Making accurate inferences from chromatin profiling experiments that involve diverse experimental parameters is challenging...
July 15, 2017: Bioinformatics
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