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https://www.readbyqxmd.com/read/28318904/dual-memory-neural-networks-for-modeling-cognitive-activities-of-humans-via-wearable-sensors
#1
Sang-Woo Lee, Chung-Yeon Lee, Dong-Hyun Kwak, Jung-Woo Ha, Jeonghee Kim, Byoung-Tak Zhang
Wearable devices, such as smart glasses and watches, allow for continuous recording of everyday life in a real world over an extended period of time or lifelong. This possibility helps better understand the cognitive behavior of humans in real life as well as build human-aware intelligent agents for practical purposes. However, modeling the human cognitive activity from wearable-sensor data stream is challenging because learning new information often results in loss of previously acquired information, causing a problem known as catastrophic forgetting...
February 20, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28318903/understanding-human-intention-by-connecting-perception-and-action-learning-in-artificial-agents
#2
Sangwook Kim, Zhibin Yu, Minho Lee
To develop an advanced human-robot interaction system, it is important to first understand how human beings learn to perceive, think, and act in an ever-changing world. In this paper, we propose an intention understanding system that uses an Object Augmented-Supervised Multiple Timescale Recurrent Neural Network (OA-SMTRNN) and demonstrate the effects of perception-action connected learning in an artificial agent, which is inspired by psychological and neurological phenomena in humans. We believe that action and perception are not isolated processes in human mental development, and argue that these psychological and neurological interactions can be replicated in a human-machine scenario...
February 11, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28316189/-noteworthy-and-controversial-problems-of-deep-anterior-lamellar-keratoplasty
#3
H Gao
Corneal transplant is the main treatment for blindness caused by corneal diseases. The common methods of corneal transplant include penetrating keratoplasty and lamellar keratoplasty. With the advances of microsurgical instruments and technology in recent years, deep anterior lamellar keratoplasty (DALK) has gradually increased. Compared with conventional penetrating keratoplasty, the optical effects of DALK are not much different, but DALK has fewer complications. So DALK is expected to become the mainstream of corneal transplantation...
March 11, 2017: [Zhonghua Yan Ke za Zhi] Chinese Journal of Ophthalmology
https://www.readbyqxmd.com/read/28315069/toolkits-and-libraries-for-deep-learning
#4
REVIEW
Bradley J Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy Kline, Kenneth Philbrick
Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data...
March 17, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28314495/global-service-learning-and-student-athletes-a-model-for-enhanced-academic-inclusion-at-the-university-of-washington
#5
Holly M Barker
BACKGROUND: The University of Washington (UW) continues to create opportunities to engage all students in transformational undergraduate educational opportunities, such as study abroad. OBJECTIVE: This article describes specific efforts to increase inclusion for student-athletes in study abroad, particularly for first-generation students, including low-income students of color. Given the overrepresentation of students of color in sports vis-à-vis the larger student body at predominantly white institutions (PWIs), like UW, service-learning in communities beyond campus boundaries provides opportunities to apply international learning to a local context and to create a continuum of learning...
November 2016: Annals of Global Health
https://www.readbyqxmd.com/read/28306716/localization-and-diagnosis-framework-for-pediatric-cataracts-based-on-slit-lamp-images-using-deep-features-of-a-convolutional-neural-network
#6
Xiyang Liu, Jiewei Jiang, Kai Zhang, Erping Long, Jiangtao Cui, Mingmin Zhu, Yingying An, Jia Zhang, Zhenzhen Liu, Zhuoling Lin, Xiaoyan Li, Jingjing Chen, Qianzhong Cao, Jing Li, Xiaohang Wu, Dongni Wang, Haotian Lin
Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets...
2017: PloS One
https://www.readbyqxmd.com/read/28303790/design-of-robust-adaptive-controller-and-feedback-error-learning-for-rehabilitation-in-parkinson-s-disease-a-simulation-study
#7
Korosh Rouhollahi, Mehran Emadi Andani, Seyed Mahdi Karbassi, Iman Izadi
Deep brain stimulation (DBS) is an efficient therapy to control movement disorders of Parkinson's tremor. Stimulation of one area of basal ganglia (BG) by DBS with no feedback is the prevalent opinion. Reduction of additional stimulatory signal delivered to the brain is the advantage of using feedback. This results in reduction of side effects caused by the excessive stimulation intensity. In fact, the stimulatory intensity of controllers is decreased proportional to reduction of hand tremor. The objective of this study is to design a new controller structure to decrease three indicators: (i) the hand tremor; (ii) the level of delivered stimulation in disease condition; and (iii) the ratio of the level of delivered stimulation in health condition to disease condition...
February 2017: IET Systems Biology
https://www.readbyqxmd.com/read/28301734/deep-learning-in-medical-image-analysis
#8
Dinggang Shen, Guorong Wu, Heung-Il Suk
This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications...
March 9, 2017: Annual Review of Biomedical Engineering
https://www.readbyqxmd.com/read/28298263/a-proof-of-principle-simulation-for-closed-loop-control-based-on-preexisting-experimental-thalamic-dbs-enhanced-instrumental-learning
#9
Ching-Fu Wang, Shih-Hung Yang, Sheng-Huang Lin, Po-Chuan Chen, Yu-Chun Lo, Han-Chi Pan, Hsin-Yi Lai, Lun-De Liao, Hui-Ching Lin, Hsu-Yan Chen, Wei-Chen Huang, Wun-Jhu Huang, You-Yin Chen
Deep brain stimulation (DBS) has been applied as an effective therapy for treating Parkinson's disease or essential tremor. Several open-loop DBS control strategies have been developed for clinical experiments, but they are limited by short battery life and inefficient therapy. Therefore, many closed-loop DBS control systems have been designed to tackle these problems by automatically adjusting the stimulation parameters via feedback from neural signals, which has been reported to reduce the power consumption...
February 24, 2017: Brain Stimulation
https://www.readbyqxmd.com/read/28297857/mean-field-message-passing-equations-in-the-hopfield-model-and-its-generalizations
#10
Marc Mézard
Motivated by recent progress in using restricted Boltzmann machines as preprocessing algorithms for deep neural network, we revisit the mean-field equations [belief-propagation and Thouless-Anderson Palmer (TAP) equations] in the best understood of such machines, namely the Hopfield model of neural networks, and we explicit how they can be used as iterative message-passing algorithms, providing a fast method to compute the local polarizations of neurons. In the "retrieval phase", where neurons polarize in the direction of one memorized pattern, we point out a major difference between the belief propagation and TAP equations: The set of belief propagation equations depends on the pattern which is retrieved, while one can use a unique set of TAP equations...
February 2017: Physical Review. E
https://www.readbyqxmd.com/read/28289601/3d-scattering-transforms-for-disease-classification-in-neuroimaging
#11
Tameem Adel, Taco Cohen, Matthan Caan, Max Welling
Classifying neurodegenerative brain diseases in MRI aims at correctly assigning discrete labels to MRI scans. Such labels usually refer to a diagnostic decision a learner infers based on what it has learned from a training sample of MRI scans. Classification from MRI voxels separately typically does not provide independent evidence towards or against a class; the information relevant for classification is only present in the form of complicated multivariate patterns (or "features"). Deep learning solves this problem by learning a sequence of non-linear transformations that result in feature representations that are better suited to classification...
2017: NeuroImage: Clinical
https://www.readbyqxmd.com/read/28289387/analyses-of-mrna-profiling-through-rna-sequencing-on-a-samp8-mouse-model-in-response-to-ginsenoside-rg1-and-rb1-treatment
#12
Shuai Zhang, Dina Zhu, Hong Li, Haijing Zhang, Chengqiang Feng, Wensheng Zhang
Ginsenoside Rg1 and Rb1 are the major ingredients in two medicines called QiShengLi (Z20027165) and QiShengJing (Z20027164) approved by China. These ingredients are believed to mitigate forgetfulness. Numerous studies have confirmed that GRg1 and GRb1 offer protection against Alzheimer's disease (AD), and our morris water maze (MWM) experiment also indicated that GRg1 and GRb1 may attenuate memory deficits in the 7-month-old SAMP8 mice; however, comprehensive understanding of their roles in AD remains limited...
2017: Frontiers in Pharmacology
https://www.readbyqxmd.com/read/28287986/deepx-deep-learning-accelerator-for-restricted-boltzmann-machine-artificial-neural-networks
#13
Lok-Won Kim
Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann machine (RBM) ANNs...
March 8, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28287966/deep-learning-segmentation-of-optical-microscopy-images-improves-3d-neuron-reconstruction
#14
Rongjian Li, Tao Zeng, Hanchuan Peng, Shuiwang Ji
Digital reconstruction, or tracing, of 3-dimensional (3D) neuron structure from microscopy images is a critical step toward reversing engineering the wiring and anatomy of a brain. Despite a number of prior attempts, this task remains very challenging, especially when images are contaminated by noises or have discontinued segments of neurite patterns. An approach for addressing such problems is to identify the locations of neuronal voxels using image segmentation methods prior to applying tracing or reconstruction techniques...
March 8, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28287963/a-dataset-and-a-technique-for-generalized-nuclear-segmentation-for-computational-pathology
#15
Neeraj Kumar, Ruchika Verma, Sanuj Sharma, Surabhi Bhargava, Abhishek Vahadane, Amit Sethi
Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques such as Otsu thresholding and watershed segmentation do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms require datasets of images in which a vast number of nuclei have been annotated...
March 6, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28286397/perforator-peroneal-artery-flap-for-tongue-reconstruction
#16
Shubhra Chauhan, Sachin Chavre, Naveen Hedne Chandrashekar, Naveen B S
INTRODUCTION: Reconstruction has evolved long way from primary closure to flaps. As time evolved, better understanding of vascularity of flap has led to the development of innovative reconstructive techniques. These flaps can be raised from various parts of the body for reconstruction and have shown least donor site morbidity. We use one such peroneal artery perforator flap for tongue reconstruction with advantage of thin pliable flap, minimal donor site morbidity and hidden scar. MATERIALS AND METHODS: Our patient 57yrs old lady underwent wide local excision with selective neck dissection...
March 2017: Journal of Maxillofacial and Oral Surgery
https://www.readbyqxmd.com/read/28285556/comparing-a-longitudinal-integrated-clerkship-with-traditional-hospital-based-rotations-in-a-rural-setting
#17
Rebecca Caygill, Mia Peardon, Catherine Waite, Julian Wright
CONTEXT: Longitudinal integrated clerkships (LIC) are widely used as an educational method, particularly in rural areas. They are good for facilitating hands-on learning and deep relationships between student, patients, and supervisors. OBJECTIVES: This study sought to examine and compare learning experience of third-year rural medical students studying specialties (women's health, aged care, child and adolescent heath, mental health, general practice) by either a traditional hospital-based rotation or a LIC in a rural general practice setting...
March 11, 2017: Medical Teacher
https://www.readbyqxmd.com/read/28282439/iterative-free-energy-optimization-for-recurrent-neural-networks-inferno
#18
Alexandre Pitti, Philippe Gaussier, Mathias Quoy
The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains...
2017: PloS One
https://www.readbyqxmd.com/read/28281832/outcomes-of-introducing-early-learners-to-interprofessional-competencies-in-a-classroom-setting
#19
Kelly S Lockeman, Sharon K Lanning, Alan W Dow, Joseph A Zorek, Deborah DiazGranados, Carole K Ivey, Shawne Soper
PROBLEM: Although interprofessional practice is important for improving healthcare delivery, there is little evidence describing interprofessional education (IPE) outcomes beyond changes in attitudes and knowledge of prelicensure learners. More rigorous evaluation of early IPE is needed to determine its impact on teaching interprofessional collaborative practice and providing a solid foundation for applying collaborative skills in the clinical environment. INTERVENTION: First-year students (N = 679) in 7 health professions programs participated in a 4-session series focusing on professional roles and responsibilities, teams and teamwork, and the healthcare system...
March 10, 2017: Teaching and Learning in Medicine
https://www.readbyqxmd.com/read/28278469/deep-hashing-for-scalable-image-search
#20
Jiwen Lu, Venice Erin Liong, Jie Zhou
In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for scalable image search. Unlike most existing binary codes learning methods which usually seek a single linear projection to map each sample into a binary feature vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the nonlinear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the developed deep network: 1) the loss between the compact real-valued code and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible...
March 3, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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