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https://www.readbyqxmd.com/read/28227963/predicting-seizures-from-local-field-potentials-recorded-via-intracortical-microelectrode-arrays
#1
Mehdi Aghagolzadeh, Leigh R Hochberg, Sydney S Cash, Wilson Truccolo, Mehdi Aghagolzadeh, Leigh R Hochberg, Sydney S Cash, Wilson Truccolo, Sydney S Cash, Wilson Truccolo, Mehdi Aghagolzadeh, Leigh R Hochberg
The need for new therapeutic interventions to treat pharmacologically resistant focal epileptic seizures has led recently to the development of closed-loop systems for seizure control. Once a seizure is predicted/detected by the system, electrical stimulation is delivered to prevent seizure initiation or spread. So far, seizure prediction/detection has been limited to tracking non-invasive electroencephalogram (EEG) or intracranial EEG (iEEG) signals. Here, we examine seizure prediction based on local field potentials (LFPs) from a small neocortical patch recorded via a 10×10 microelectrode array implanted in a patient with focal seizures...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226976/thorax-disease-diagnosis-using-deep-convolutional-neural-network
#2
Jie Chen, Xianbiao Qi, Osmo Tervonen, Olli Silven, Guoying Zhao, Matti Pietikainen, Jie Chen, Xianbiao Qi, Osmo Tervonen, Olli Silven, Guoying Zhao, Matti Pietikainen, Osmo Tervonen, Xianbiao Qi, Jie Chen, Matti Pietikainen, Olli Silven, Guoying Zhao
Computer aided diagnosis (CAD) is an important issue, which can significantly improve the efficiency of doctors. In this paper, we propose a deep convolutional neural network (CNN) based method for thorax disease diagnosis. We firstly align the images by matching the interest points between the images, and then enlarge the dataset by using Gaussian scale space theory. After that we use the enlarged dataset to train a deep CNN model and apply the obtained model for the diagnosis of new test data. Our experimental results show our method achieves very promising results...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226759/melanoma-detection-by-analysis-of-clinical-images-using-convolutional-neural-network
#3
E Nasr-Esfahani, S Samavi, N Karimi, S M R Soroushmehr, M H Jafari, K Ward, K Najarian, E Nasr-Esfahani, S Samavi, N Karimi, S M R Soroushmehr, M H Jafari, K Ward, K Najarian, M H Jafari, S M R Soroushmehr, E Nasr-Esfahani, S Samavi, K Najarian, N Karimi, K Ward
Melanoma, most threatening type of skin cancer, is on the rise. In this paper an implementation of a deep-learning system on a computer server, equipped with graphic processing unit (GPU), is proposed for detection of melanoma lesions. Clinical (non-dermoscopic) images are used in the proposed system, which could assist a dermatologist in early diagnosis of this type of skin cancer. In the proposed system, input clinical images, which could contain illumination and noise effects, are preprocessed in order to reduce such artifacts...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226742/a-cnn-based-neurobiology-inspired-approach-for-retinal-image-quality-assessment
#4
Dwarikanath Mahapatra, Pallab K Roy, Suman Sedai, Rahil Garnavi, Dwarikanath Mahapatra, Pallab K Roy, Suman Sedai, Rahil Garnavi, Suman Sedai, Pallab K Roy, Rahil Garnavi, Dwarikanath Mahapatra
Retinal image quality assessment (IQA) algorithms use different hand crafted features for training classifiers without considering the working of the human visual system (HVS) which plays an important role in IQA. We propose a convolutional neural network (CNN) based approach that determines image quality using the underlying principles behind the working of the HVS. CNNs provide a principled approach to feature learning and hence higher accuracy in decision making. Experimental results demonstrate the superior performance of our proposed algorithm over competing methods...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226621/encoding-physiological-signals-as-images-for-affective-state-recognition-using-convolutional-neural-networks
#5
Guangliang Yu, Xiang Li, Dawei Song, Xiaozhao Zhao, Peng Zhang, Yuexian Hou, Bin Hu, Guangliang Yu, Xiang Li, Dawei Song, Xiaozhao Zhao, Peng Zhang, Yuexian Hou, Bin Hu, Xiaozhao Zhao, Yuexian Hou, Xiang Li, Bin Hu, Peng Zhang, Dawei Song, Guangliang Yu
Affective state recognition based on multiple modalities of physiological signals has been a hot research topic. Traditional methods require designing hand-crafted features based on domain knowledge, which is time-consuming and has not achieved a satisfactory performance. On the other hand, conducting classification on raw signals directly can also cause some problems, such as the interference of noise and the curse of dimensionality. To address these problems, we propose a novel approach that encodes different modalities of data as images and use convolutional neural networks (CNN) to perform the affective state recognition task...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226583/recent-machine-learning-advancements-in-sensor-based-mobility-analysis-deep-learning-for-parkinson-s-disease-assessment
#6
Bjoern M Eskofier, Sunghoon I Lee, Jean-Francois Daneault, Fatemeh N Golabchi, Gabriela Ferreira-Carvalho, Gloria Vergara-Diaz, Stefano Sapienza, Gianluca Costante, Jochen Klucken, Thomas Kautz, Paolo Bonato, Bjoern M Eskofier, Sunghoon I Lee, Jean-Francois Daneault, Fatemeh N Golabchi, Gabriela Ferreira-Carvalho, Gloria Vergara-Diaz, Stefano Sapienza, Gianluca Costante, Jochen Klucken, Thomas Kautz, Paolo Bonato, Fatemeh N Golabchi, Gianluca Costante, Gloria Vergara-Diaz, Paolo Bonato, Gabriela Ferreira-Carvalho, Jean-Francois Daneault, Bjoern M Eskofier, Jochen Klucken, Sunghoon I Lee, Thomas Kautz, Stefano Sapienza
The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226581/automatic-lumbar-vertebrae-detection-based-on-feature-fusion-deep-learning-for-partial-occluded-c-arm-x-ray-images
#7
Yang Li, Wei Liang, Yinlong Zhang, Haibo An, Jindong Tan, Yang Li, Wei Liang, Yinlong Zhang, Haibo An, Jindong Tan, Yang Li, Wei Liang, Jindong Tan, Yinlong Zhang, Haibo An
Automatic and accurate lumbar vertebrae detection is an essential step of image-guided minimally invasive spine surgery (IG-MISS). However, traditional methods still require human intervention due to the similarity of vertebrae, abnormal pathological conditions and uncertain imaging angle. In this paper, we present a novel convolutional neural network (CNN) model to automatically detect lumbar vertebrae for C-arm X-ray images. Training data is augmented by DRR and automatic segmentation of ROI is able to reduce the computational complexity...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226580/vessel-extraction-in-x-ray-angiograms-using-deep-learning
#8
E Nasr-Esfahani, S Samavi, N Karimi, S M R Soroushmehr, K Ward, M H Jafari, B Felfeliyan, B Nallamothu, K Najarian, E Nasr-Esfahani, S Samavi, N Karimi, S M R Soroushmehr, K Ward, M H Jafari, B Felfeliyan, B Nallamothu, K Najarian, M H Jafari, S M R Soroushmehr, E Nasr-Esfahani, S Samavi, K Najarian, B Nallamothu, N Karimi, K Ward, B Felfeliyan
Coronary artery disease (CAD) is the most common type of heart disease which is the leading cause of death all over the world. X-ray angiography is currently the gold standard imaging technique for CAD diagnosis. These images usually suffer from low quality and presence of noise. Therefore, vessel enhancement and vessel segmentation play important roles in CAD diagnosis. In this paper a deep learning approach using convolutional neural networks (CNN) is proposed for detecting vessel regions in angiography images...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226579/a-deep-convolutional-neural-network-for-bleeding-detection-in-wireless-capsule-endoscopy-images
#9
Xiao Jia, Max Q-H Meng, Xiao Jia, Max Q-H Meng, Xiao Jia, Max Q-H Meng
Wireless Capsule Endoscopy (WCE) is a standard non-invasive modality for small bowel examination. Recently, the development of computer-aided diagnosis (CAD) systems for gastrointestinal (GI) bleeding detection in WCE image videos has become an active research area with the goal of relieving the workload of physicians. Existing methods based primarily on handcrafted features usually give insufficient accuracy for bleeding detection, due to their limited capability of feature representation. In this paper, we present a new automatic bleeding detection strategy based on a deep convolutional neural network and evaluate our method on an expanded dataset of 10,000 WCE images...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226528/automatic-grasp-selection-using-a-camera-in-a-hand-prosthesis
#10
Joseph DeGol, Aadeel Akhtar, Bhargava Manja, Timothy Bretl, Joseph DeGol, Aadeel Akhtar, Bhargava Manja, Timothy Bretl, Joseph DeGol, Bhargava Manja, Timothy Bretl, Aadeel Akhtar
In this paper, we demonstrate how automatic grasp selection can be achieved by placing a camera in the palm of a prosthetic hand and training a convolutional neural network on images of objects with corresponding grasp labels. Our labeled dataset is built from common graspable objects curated from the ImageNet dataset and from images captured from our own camera that is placed in the hand. We achieve a grasp classification accuracy of 93.2% and show through realtime grasp selection that using a camera to augment current electromyography controlled prosthetic hands may be useful...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28225827/a-convolutional-neural-network-for-steady-state-visual-evoked-potential-classification-under-ambulatory-environment
#11
No-Sang Kwak, Klaus-Robert Müller, Seong-Whan Lee
The robust analysis of neural signals is a challenging problem. Here, we contribute a convolutional neural network (CNN) for the robust classification of a steady-state visual evoked potentials (SSVEPs) paradigm. We measure electroencephalogram (EEG)-based SSVEPs for a brain-controlled exoskeleton under ambulatory conditions in which numerous artifacts may deteriorate decoding. The proposed CNN is shown to achieve reliable performance under these challenging conditions. To validate the proposed method, we have acquired an SSVEP dataset under two conditions: 1) a static environment, in a standing position while fixated into a lower-limb exoskeleton and 2) an ambulatory environment, walking along a test course wearing the exoskeleton (here, artifacts are most challenging)...
2017: PloS One
https://www.readbyqxmd.com/read/28223187/deepnat-deep-convolutional-neural-network-for-segmenting-neuroanatomy
#12
Christian Wachinger, Martin Reuter, Tassilo Klein
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground...
February 18, 2017: NeuroImage
https://www.readbyqxmd.com/read/28222011/automatic-sleep-stage-classification-of-single-channel-eeg-by-using-complex-valued-convolutional-neural-network
#13
Junming Zhang, Yan Wu
Many systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist...
February 21, 2017: Biomedizinische Technik. Biomedical Engineering
https://www.readbyqxmd.com/read/28221995/deep-cascade-cascading-3d-deep-neural-networks-for-fast-anomaly-detection-and-localization-in-crowded-scenes
#14
Mohammad Sabokrou, Mohsen Fayyaz, Mahmood Fathy, Reinhard Klette
This paper proposes a fast and reliable method for anomaly detection and localization in video data showing crowded scenes. Time-efficient anomaly localization is an ongoing challenge and subject of this paper. We propose a cubicpatch- based method, characterised by a cascade of classifiers, which makes use of an advanced feature-learning approach. Our cascade of classifiers has two main stages. First, a light but deep 3D auto-encoder is used for early identification of "many" normal cubic patches. This deep network operates on small cubic patches as being the first stage, before carefully resizing remaining candidates of interest, and evaluating those at the second stage using a more complex and deeper 3D convolutional neural network (CNN)...
February 17, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28212106/view-based-3-d-model-retrieval-a-benchmark
#15
An-An Liu, Wei-Zhi Nie, Yue Gao, Yu-Ting Su
View-based 3-D model retrieval is one of the most important techniques in numerous applications of computer vision. While many methods have been proposed in recent years, to the best of our knowledge, there is no benchmark to evaluate the state-of-the-art methods. To tackle this problem, we systematically investigate and evaluate the related methods by: 1) proposing a clique graph-based method and 2) reimplementing six representative methods. Moreover, we concurrently evaluate both hand-crafted visual features and deep features on four popular datasets (NTU60, NTU216, PSB, and ETH) and one challenging real-world multiview model dataset (MV-RED) prepared by our group with various evaluation criteria to understand how these algorithms perform...
February 15, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28211462/zebrafish-tracking-using-convolutional-neural-networks
#16
Zhiping Xu, Xi En Cheng
Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans...
February 17, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28208587/vehicle-detection-in-aerial-images-based-on-region-convolutional-neural-networks-and-hard-negative-example-mining
#17
Tianyu Tang, Shilin Zhou, Zhipeng Deng, Huanxin Zou, Lin Lei
Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well...
February 10, 2017: Sensors
https://www.readbyqxmd.com/read/28207407/deep-pain-exploiting-long-short-term-memory-networks-for-facial-expression-classification
#18
Pau Rodriguez, Guillem Cucurull, Jordi Gonalez, Josep M Gonfaus, Kamal Nasrollahi, Thomas B Moeslund, F Xavier Roca
Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data...
February 9, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28207398/acoustic-classification-and-optimization-for-multi-modal-rendering-of-real-world-scenes
#19
Carl Schissler, Christian Loftin, Dinesh Manocha
We present a novel algorithm to generate virtual acoustic effects in captured 3D models of real-world scenes for multimodal augmented reality. We leverage recent advances in 3D scene reconstruction in order to automatically compute acoustic material properties. Our technique consists of a two-step procedure that first applies a convolutional neural network (CNN) to estimate the acoustic material properties, including frequency-dependent absorption coefficients, that are used for interactive sound propagation...
February 9, 2017: IEEE Transactions on Visualization and Computer Graphics
https://www.readbyqxmd.com/read/28207396/hd-mtl-hierarchical-deep-multi-task-learning-for-large-scale-visual-recognition
#20
Jianping Fan, Tianyi Zhao, Zhenzhong Kuang, Yu Zheng, Ji Zhang, Jun Yu, Jinye Peng
In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically...
February 9, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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