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https://www.readbyqxmd.com/read/28727737/improving-deep-convolutional-neural-networks-with-mixed-maxout-units
#1
Hui-Zhen Zhao, Fu-Xian Liu, Long-Yue Li
Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities...
2017: PloS One
https://www.readbyqxmd.com/read/28723578/convolutional-neural-network-based-encoding-and-decoding-of-visual-object-recognition-in-space-and-time
#2
REVIEW
K Seeliger, M Fritsche, U Güçlü, S Schoenmakers, J-M Schoffelen, S E Bosch, M A J van Gerven
Representations learned by deep convolutional neural networks (CNNs) for object recognition are a widely investigated model of the processing hierarchy in the human visual system. Using functional magnetic resonance imaging, CNN representations of visual stimuli have previously been shown to correspond to processing stages in the ventral and dorsal streams of the visual system. Whether this correspondence between models and brain signals also holds for activity acquired at high temporal resolution has been explored less exhaustively...
July 16, 2017: NeuroImage
https://www.readbyqxmd.com/read/28720701/de-novo-peptide-sequencing-by-deep-learning
#3
Ngoc Hieu Tran, Xianglilan Zhang, Lei Xin, Baozhen Shan, Ming Li
De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing...
July 18, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28717579/deep-learning-based-automated-segmentation-of-macular-edema-in-optical-coherence-tomography
#4
Cecilia S Lee, Ariel J Tyring, Nicolaas P Deruyter, Yue Wu, Ariel Rokem, Aaron Y Lee
Evaluation of clinical images is essential for diagnosis in many specialties. Therefore the development of computer vision algorithms to help analyze biomedical images will be important. In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions. We developed a convolutional neural network (CNN) that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians. Using 1,289 OCT images, the CNN segmented images with a 0.911 cross-validated Dice coefficient, compared with segmentations by experts...
July 1, 2017: Biomedical Optics Express
https://www.readbyqxmd.com/read/28717568/robust-total-retina-thickness-segmentation-in-optical-coherence-tomography-images-using-convolutional-neural-networks
#5
Freerk G Venhuizen, Bram van Ginneken, Bart Liefers, Mark J J P van Grinsven, Sascha Fauser, Carel Hoyng, Thomas Theelen, Clara I Sánchez
We developed a fully automated system using a convolutional neural network (CNN) for total retina segmentation in optical coherence tomography (OCT) that is robust to the presence of severe retinal pathology. A generalized U-net network architecture was introduced to include the large context needed to account for large retinal changes. The proposed algorithm outperformed qualitative and quantitatively two available algorithms. The algorithm accurately estimated macular thickness with an error of 14.0 ± 22...
July 1, 2017: Biomedical Optics Express
https://www.readbyqxmd.com/read/28715341/an-automatic-detection-system-of-lung-nodule-based-on-multi-group-patch-based-deep-learning-network
#6
Hongyang Jiang, He Ma, Wei Qian, Mengdi Gao, Yan Li
High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography (CT) image transformation, the lung nodule segmentation and the feature extraction, to construct a whole CADe system...
July 14, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28710497/deep-learning-based-radiomics-dlr-and-its-usage-in-noninvasive-idh1-prediction-for-low-grade-glioma
#7
Zeju Li, Yuanyuan Wang, Jinhua Yu, Yi Guo, Wei Cao
Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in a dataset of 151 patients with low-grade glioma. A modified convolutional neural network (CNN) structure with 6 convolutional layers and a fully connected layer with 4096 neurons was used to segment tumors. Instead of calculating image features from segmented images, as typically performed for normal radiomics approaches, image features were obtained by normalizing the information of the last convolutional layers of the CNN...
July 14, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28708557/blind-deep-s3d-image-quality-evaluation-via-local-to-global-feature-aggregation
#8
Heeseok Oh, Sewoong Ahn, Jongyoo Kim, Sanghoon Lee
Previously, no-reference (NR) stereoscopic 3D (S3D) image quality assessment (IQA) algorithms have been limited to the extraction of reliable hand-crafted features based on an understanding of the insufficiently revealed human visual system or natural scene statistics. Furthermore, compared with full-reference (FR) S3D IQA metrics, it is difficult to achieve competitive quality score predictions using the extracted features, which are not optimized with respect to human opinion. To cope with this limitation of the conventional approach, we introduce a novel deep learning scheme for NR S3D IQA in terms of local to global feature aggregation...
July 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708555/going-deeper-with-contextual-cnn-for-hyperspectral-image-classification
#9
Hyungtae Lee, Heesung Kwon
In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatio-spectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline...
July 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708546/sononet-real-time-detection-and-localisation-of-fetal-standard-scan-planes-in-freehand-ultrasound
#10
Christian F Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew, Tara P Fletcher, Sandra Smith, Lisa M Koch, Bernhard Kainz, Daniel Rueckert
Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box...
July 11, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28707123/visual-properties-and-memorising-scenes-effects-of-image-space-sparseness-and-uniformity
#11
Jiří Lukavský, Filip Děchtěrenko
Previous studies have demonstrated that humans have a remarkable capacity to memorise a large number of scenes. The research on memorability has shown that memory performance can be predicted by the content of an image. We explored how remembering an image is affected by the image properties within the context of the reference set, including the extent to which it is different from its neighbours (image-space sparseness) and if it belongs to the same category as its neighbours (uniformity). We used a reference set of 2,048 scenes (64 categories), evaluated pairwise scene similarity using deep features from a pretrained convolutional neural network (CNN), and calculated the image-space sparseness and uniformity for each image...
July 13, 2017: Attention, Perception & Psychophysics
https://www.readbyqxmd.com/read/28706534/bag-of-visual-words-model-with-deep-spatial-features-for-geographical-scene-classification
#12
Jiangfan Feng, Yuanyuan Liu, Lin Wu
With the popular use of geotagging images, more and more research efforts have been placed on geographical scene classification. In geographical scene classification, valid spatial feature selection can significantly boost the final performance. Bag of visual words (BoVW) can do well in selecting feature in geographical scene classification; nevertheless, it works effectively only if the provided feature extractor is well-matched. In this paper, we use convolutional neural networks (CNNs) for optimizing proposed feature extractor, so that it can learn more suitable visual vocabularies from the geotagging images...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28706185/deep-learning-for-fully-automated-localization-and-segmentation-of-rectal-cancer-on-multiparametric-mr
#13
Stefano Trebeschi, Joost J M van Griethuysen, Doenja M J Lambregts, Max J Lahaye, Chintan Parmer, Frans C H Bakers, Nicky H G M Peters, Regina G H Beets-Tan, Hugo J W L Aerts
Multiparametric Magnetic Resonance Imaging (MRI) can provide detailed information of the physical characteristics of rectum tumours. Several investigations suggest that volumetric analyses on anatomical and functional MRI contain clinically valuable information. However, manual delineation of tumours is a time consuming procedure, as it requires a high level of expertise. Here, we evaluate deep learning methods for automatic localization and segmentation of rectal cancers on multiparametric MR imaging. MRI scans (1...
July 13, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28701911/an-event-driven-classifier-for-spiking-neural-networks-fed-with-synthetic-or-dynamic-vision-sensor-data
#14
Evangelos Stromatias, Miguel Soto, Teresa Serrano-Gotarredona, Bernabé Linares-Barranco
This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28698556/location-sensitive-deep-convolutional-neural-networks-for-segmentation-of-white-matter-hyperintensities
#15
Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Inge W M van Uden, Clara I Sanchez, Geert Litjens, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel
The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to incorporate the anatomical location in their decision making process, hindering success in some medical image analysis tasks. In this paper, to integrate the anatomical location information into the network, we propose several deep CNN architectures that consider multi-scale patches or take explicit location features while training...
July 11, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28698475/pedestrian-detection-based-on-adaptive-selection-of-visible-light-or-far-infrared-light-camera-image-by-fuzzy-inference-system-and-convolutional-neural-network-based-verification
#16
Jin Kyu Kang, Hyung Gil Hong, Kang Ryoung Park
A number of studies have been conducted to enhance the pedestrian detection accuracy of intelligent surveillance systems. However, detecting pedestrians under outdoor conditions is a challenging problem due to the varying lighting, shadows, and occlusions. In recent times, a growing number of studies have been performed on visible light camera-based pedestrian detection systems using a convolutional neural network (CNN) in order to make the pedestrian detection process more resilient to such conditions. However, visible light cameras still cannot detect pedestrians during nighttime, and are easily affected by shadows and lighting...
July 8, 2017: Sensors
https://www.readbyqxmd.com/read/28698466/multi-national-banknote-classification-based-on-visible-light-line-sensor-and-convolutional-neural-network
#17
Tuyen Danh Pham, Dong Eun Lee, Kang Ryoung Park
Automatic recognition of banknotes is applied in payment facilities, such as automated teller machines (ATMs) and banknote counters. Besides the popular approaches that focus on studying the methods applied to various individual types of currencies, there have been studies conducted on simultaneous classification of banknotes from multiple countries. However, their methods were conducted with limited numbers of banknote images, national currencies, and denominations. To address this issue, we propose a multi-national banknote classification method based on visible-light banknote images captured by a one-dimensional line sensor and classified by a convolutional neural network (CNN) considering the size information of each denomination...
July 8, 2017: Sensors
https://www.readbyqxmd.com/read/28696688/convolutional-embedding-of-attributed-molecular-graphs-for-physical-property-prediction
#18
Connor W Coley, Regina Barzilay, William H Green, Tommi S Jaakkola, Klavs F Jensen
The task of learning an expressive molecular representation is central to developing quantitative structure-activity and property relationships. Traditional approaches rely on group additivity rules, empirical measurements or parameters, or generation of thousands of descriptors. In this paper, we employ a convolutional neural network for this embedding task by treating molecules as undirected graphs with attributed nodes and edges. Simple atom and bond attributes are used to construct atom-specific feature vectors that take into account the local chemical environment using different neighborhood radii...
July 11, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28695342/thyroid-nodule-classification-in-ultrasound-images-by-fine-tuning-deep-convolutional-neural-network
#19
Jianning Chi, Ekta Walia, Paul Babyn, Jimmy Wang, Gary Groot, Mark Eramian
With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts...
July 10, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28692977/low-rank-matrix-completion-to-reconstruct-incomplete-rendering-images
#20
Ping Liu, John P Lewis, Taehyun Rhee
Path tracing provides photo-realistic rendering in many applications but intermediate previsualization often suffers from distracting noise. Since the fundamental underlying problem is insufficient samples, we exploit the coherence of the visual signal to reconstruct missing samples, using a low-rank matrix completion framework. We present novel methods to construct low rank matrices for incomplete images including missing pixel, missing sub-pixel, and multi-frame scenarios. A convolutional neural network provides fast pre-completion for initialising missing values, and subsequent weighted nuclear norm minimisation (WNNM) with a parameter adjustment strategy (PAWNNM) efficiently recovers missing values even in high frequency details...
July 3, 2017: IEEE Transactions on Visualization and Computer Graphics
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