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https://www.readbyqxmd.com/read/28434004/what-do-medical-students-know-about-deep-brain-stimulation
#1
Andreas Wloch, Assel Saryyeva, Hans E Heissler, Christoph Schrader, H Holger Capelle, Joachim K Krauss
BACKGROUND: Deep brain stimulation (DBS) is an established therapy for movement disorders. It is currently under investigation in neuropsychiatric disorders. Neurophobia is a common phenomenon that might have a negative impact in medical education. Little is known about medical students' knowledge about DBS when they enter university and what they learn about it during their medical formation. METHODS: A 10-item questionnaire was designed. Questions addressed indications for DBS, costs of DBS, complications, the percentage of Parkinson disease (PD) patients who might profit from DBS, etc...
April 22, 2017: Stereotactic and Functional Neurosurgery
https://www.readbyqxmd.com/read/28433431/application-of-structured-support-vector-machine-backpropagation-to-a-convolutional-neural-network-for-human-pose-estimation
#2
Peerajak Witoonchart, Prabhas Chongstitvatana
In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer...
February 16, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28431201/teaching-neuroanatomy-using-computer-aided-learning-what-makes-for-successful-outcomes
#3
Elena Svirko, Jane Mellanby
Computer-aided learning (CAL) is an integral part of many medical courses. The neuroscience course at Oxford University for medical students includes CAL course of neuroanatomy. CAL is particularly suited to this since neuroanatomy requires much detailed three-dimensional visualization, which can be presented on screen. The CAL course was evaluated using the concept of approach to learning. The aims of university teaching are congruent with the deep approach-seeking meaning and relating new information to previous knowledge-rather than to the surface approach of concentrating on rote learning of detail...
April 21, 2017: Anatomical Sciences Education
https://www.readbyqxmd.com/read/28430977/deep-mining-heterogeneous-networks-of-biomedical-linked-data-to-predict-novel-drug-target-associations
#4
Nansu Zong, Hyeoneui Kim, Victoria Ngo, Olivier Harismendy
Motivation: A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction. Results: We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure...
April 18, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28430949/capturing-non-local-interactions-by-long-short-term-memory-bidirectional-recurrent-neural-networks-for-improving-prediction-of-protein-secondary-structure-backbone-angles-contact-numbers-and-solvent-accessibility
#5
Rhys Heffernan, Yuedong Yang, Kuldip Paliwal, Yaoqi Zhou
Motivation: The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions between amino acid residues that are close in three-dimensional structural space but far from each other in their sequence positions. All existing machine-learning techniques relied on a sliding window of 10-20 amino acid residues to capture some "short to intermediate" non-local interactions...
April 18, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28425947/deep-count-fruit-counting-based-on-deep-simulated-learning
#6
Maryam Rahnemoonfar, Clay Sheppard
Recent years have witnessed significant advancement in computer vision research based on deep learning. Success of these tasks largely depends on the availability of a large amount of training samples. Labeling the training samples is an expensive process. In this paper, we present a simulated deep convolutional neural network for yield estimation. Knowing the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force...
April 20, 2017: Sensors
https://www.readbyqxmd.com/read/28423846/a-case-study-on-sepsis-using-pubmed-and-deep-learning-for-ontology-learning
#7
Mercedes Arguello Casteleiro, Diego Maseda Fernandez, George Demetriou, Warren Read, Maria Jesus Fernandez Prieto, Julio Des Diz, Goran Nenadic, John Keane, Robert Stevens
We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora. Term extraction is used as the first step of an ontology learning process that aims to (semi-)automatic annotation of biomedical concepts and relations from more than 300K PubMed titles and abstracts. We experimented with both traditional distributional semantics methods such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) as well as the neural language models CBOW and Skip-gram from Deep Learning...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28422661/correlated-topic-vector-for-scene-classification
#8
Pengxu Wei, Fei Qin, Fang Wan, Yi Zhu, Jianbin Jiao, Qixiang Ye
Scene images usually involve semantic correlations, particularly when considering large-scale image datasets. This paper proposes a novel generative image representation, Correlated Topic Vector, to model such semantic correlations. Oriented from the correlated topic model, Correlated Topic Vector intends to naturally utilize the correlations among topics which are seldom considered in the conventional feature encoding, e.g., Fisher Vector, but do exist in scene images. It is expected that the involvement of correlations can increase the discriminative capability of the learned generative model and consequently improve the recognition accuracy...
April 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28422152/towards-automatic-pulmonary-nodule-management-in-lung-cancer-screening-with-deep-learning
#9
Francesco Ciompi, Kaman Chung, Sarah J van Riel, Arnaud Arindra Adiyoso Setio, Paul K Gerke, Colin Jacobs, Ernst Th Scholten, Cornelia Schaefer-Prokop, Mathilde M W Wille, Alfonso Marchianò, Ugo Pastorino, Mathias Prokop, Bram van Ginneken
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup...
April 19, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28420678/automated-analysis-of-high-content-microscopy-data-with-deep-learning
#10
Oren Z Kraus, Ben T Grys, Jimmy Ba, Yolanda Chong, Brendan J Frey, Charles Boone, Brenda J Andrews
Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization...
April 18, 2017: Molecular Systems Biology
https://www.readbyqxmd.com/read/28420443/tau-phosphorylation-induced-by-severe-closed-head-traumatic-brain-injury-is-linked-to-the-cellular-prion-protein
#11
Richard Rubenstein, Binggong Chang, Natalia Grinkina, Eleanor Drummond, Peter Davies, Meir Ruditzky, Deep Sharma, Kevin Wang, Thomas Wisniewski
Studies in vivo and in vitro have suggested that the mechanism underlying Alzheimer's disease (AD) neuropathogenesis is initiated by an interaction between the cellular prion protein (PrP(C)) and amyloid-β oligomers (Aβo). This PrP(C)-Aβo complex activates Fyn kinase which, in turn, hyperphosphorylates tau (P-Tau) resulting in synaptic dysfunction, neuronal loss and cognitive deficits. AD transgenic mice lacking PrP(C) accumulate Aβ, but show normal survival and no loss of spatial learning and memory suggesting that PrP(C) functions downstream of Aβo production but upstream of intracellular toxicity within neurons...
April 18, 2017: Acta Neuropathologica Communications
https://www.readbyqxmd.com/read/28420405/do-international-health-partnerships-contribute-to-reverse-innovation-a-mixed-methods-study-of-thet-supported-partnerships-in-the-uk
#12
Kavian Kulasabanathan, Hamdi Issa, Yasser Bhatti, Matthew Prime, Jacqueline Del Castillo, Ara Darzi, Matthew Harris
BACKGROUND: International health partnerships (IHPs) are changing, with an increased emphasis on mutual accountability and joint agenda setting for both the high- and the low- or middle-income country (LMIC) partners. There is now an important focus on the bi-directionality of learning however for the UK partners, this typically focuses on learning at the individual level, through personal and professional development. We sought to evaluate whether this learning also takes the shape of 'Reverse Innovation' -when an idea conceived in a low-income country is subsequently adopted in a higher-income country...
April 18, 2017: Globalization and Health
https://www.readbyqxmd.com/read/28419223/removal-of-batch-effects-using-distribution-matching-residual-networks
#13
Uri Shaham, Kelly P Stanton, Jun Zhao, Huamin Li, Khadir Raddassi, Ruth Montgomery, Yuval Kluger
Motivation: Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument, and random measurement errors. Several novel biological technologies, such as mass cytometry and single-cell RNA-seq, are plagued with systematic errors that may severely affect statistical analysis if the data is not properly calibrated...
April 13, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28418027/accurate-and-reproducible-invasive-breast-cancer-detection-in-whole-slide-images-a-deep-learning-approach-for-quantifying-tumor-extent
#14
Angel Cruz-Roa, Hannah Gilmore, Ajay Basavanhally, Michael Feldman, Shridar Ganesan, Natalie N C Shih, John Tomaszewski, Fabio A González, Anant Madabhushi
With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners...
April 18, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28413974/identification-of-cell-cycle-regulated-genes-by-convolutional-neural-network
#15
Chenglin Liu, Peng Cui, Tao Huang
BACKGROUND: The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big problems in cell cycle-regulated gene detection. METHODS: Here, a computational framework called DLGene was proposed for cell cycle-regulated gene detection. It is based on the convolutional neural network, a deep learning algorithm representing raw form of data pattern without assumption of their distribution...
April 17, 2017: Combinatorial Chemistry & High Throughput Screening
https://www.readbyqxmd.com/read/28412667/nurses-perceptions-of-the-impact-of-team-based-learning-participation-on-learning-style-team-behaviours-and-clinical-performance-an-exploration-of-written-reflections
#16
Elizabeth Oldland, Judy Currey, Julie Considine, Josh Allen
Team-Based Learning (TBL) is a teaching strategy designed to promote problem solving, critical thinking and effective teamwork and communication skills; attributes essential for safe healthcare. The aim was to explore postgraduate student perceptions of the role of TBL in shaping learning style, team skills, and professional and clinical behaviours. An exploratory descriptive approach was selected. Critical care students were invited to provide consent for the use for research purposes of written reflections submitted for course work requirements...
March 30, 2017: Nurse Education in Practice
https://www.readbyqxmd.com/read/28412572/quantitative-analysis-of-patients-with-celiac-disease-by-video-capsule-endoscopy-a-deep-learning-method
#17
Teng Zhou, Guoqiang Han, Bing Nan Li, Zhizhe Lin, Edward J Ciaccio, Peter H Green, Jing Qin
BACKGROUND: Celiac disease is one of the most common diseases in the world. Capsule endoscopy is an alternative way to visualize the entire small intestine without invasiveness to the patient. It is useful to characterize celiac disease, but hours are need to manually analyze the retrospective data of a single patient. Computer-aided quantitative analysis by a deep learning method helps in alleviating the workload during analysis of the retrospective videos. METHOD: Capsule endoscopy clips from 6 celiac disease patients and 5 controls were preprocessed for training...
April 8, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28410981/predicting-healthcare-trajectories-from-medical-records-a-deep-learning-approach
#18
Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh
Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records...
April 11, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28410513/representation-learning-via-dual-autoencoder-for-recommendation
#19
Fuzhen Zhuang, Zhiqiang Zhang, Mingda Qian, Chuan Shi, Xing Xie, Qing He
Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And many subsequent works consider external information, e.g., social relationships of users and items' attributions, to improve the recommendation performance under the matrix factorization framework. However, matrix factorization methods may not make full use of the limited information from rating or check-in matrices, and achieve unsatisfying results...
March 27, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28410112/epithelium-stroma-classification-via-convolutional-neural-networks-and-unsupervised-domain-adaptation-in-histopathological-images
#20
Yue Huang, Han Zheng, Chi Liu, Xinghao Ding, Gustavo Rohde
Epithelium-stroma classification is a necessary preprocessing step in histopathological image analysis. Current deep learning based recognition methods for histology data require collection of large volumes of labeled data in order to train a new neural network when there are changes to the image acquisition procedure. However, it is extremely expensive for pathologists to manually label sufficient volumes of data for each pathology study in a professional manner, which results in limitations in real-world applications...
April 6, 2017: IEEE Journal of Biomedical and Health Informatics
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