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https://www.readbyqxmd.com/read/28634789/detection-and-grading-of-prostate-cancer-using-temporal-enhanced-ultrasound-combining-deep-neural-networks-and-tissue-mimicking-simulations
#1
Shekoofeh Azizi, Sharareh Bayat, Pingkun Yan, Amir Tahmasebi, Guy Nir, Jin Tae Kwak, Sheng Xu, Storey Wilson, Kenneth A Iczkowski, M Scott Lucia, Larry Goldenberg, Septimiu E Salcudean, Peter A Pinto, Bradford Wood, Purang Abolmaesumi, Parvin Mousavi
PURPOSE  : Temporal Enhanced Ultrasound (TeUS) has been proposed as a new paradigm for tissue characterization based on a sequence of ultrasound radio frequency (RF) data. We previously used TeUS to successfully address the problem of prostate cancer detection in the fusion biopsies. METHODS  : In this paper, we use TeUS to address the problem of grading prostate cancer in a clinical study of 197 biopsy cores from 132 patients. Our method involves capturing high-level latent features of TeUS with a deep learning approach followed by distribution learning to cluster aggressive cancer in a biopsy core...
June 20, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28634774/learning-from-synthetic-models-of-extracellular-matrix-differential-binding-of-wild-type-and-amyloidogenic-human-apolipoprotein-a-i-to-hydrogels-formed-from-molecules-having-charges-similar-to-those-found-in-natural-gags
#2
Silvana A Rosú, Leandro Toledo, Bruno F Urbano, Susana A Sanchez, Graciela C Calabrese, M Alejandra Tricerri
Among other components of the extracellular matrix (ECM), glycoproteins and glycosaminoglycans (GAGs) have been strongly associated to the retention or misfolding of different proteins inducing the formation of deposits in amyloid diseases. The composition of these molecules is highly diverse and a key issue seems to be the equilibrium between physiological and pathological events. In order to have a model in which the composition of the matrix could be finely controlled, we designed and synthesized crosslinked hydrophilic polymers, the so-called hydrogels varying the amounts of negative charges and hydroxyl groups that are prevalent in GAGs...
June 20, 2017: Protein Journal
https://www.readbyqxmd.com/read/28634344/omniga-optimized-omnivariate-decision-trees-for-generalizable-classification-models
#3
Arturo Magana-Mora, Vladimir B Bajic
Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods...
June 20, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28631143/post-operative-3d-ct-feedback-improves-accuracy-and-precision-in-the-learning-curve-of-anatomic-acl-femoral-tunnel-placement
#4
Luigi Sirleo, Massimo Innocenti, Matteo Innocenti, Roberto Civinini, Christian Carulli, Fabrizio Matassi
PURPOSE: To evaluate the feedback from post-operative three-dimensional computed tomography (3D-CT) on femoral tunnel placement in the learning process, to obtain an anatomic anterior cruciate ligament (ACL) reconstruction. METHODS: A series of 60 consecutive patients undergoing primary ACL reconstruction using autologous hamstrings single-bundle outside-in technique were prospectively included in the study. ACL reconstructions were performed by the same trainee-surgeon during his learning phase of anatomic ACL femoral tunnel placement...
June 19, 2017: Knee Surgery, Sports Traumatology, Arthroscopy: Official Journal of the ESSKA
https://www.readbyqxmd.com/read/28627322/orienting-to-see-what-s-important-learn-to-ignore-the-irrelevant
#5
Mitchell Rabinowitz, Jaclin Gerstel-Friedman
BACKGROUND AND AIM: The current study used a triad judgment task to assess whether blocking by comparison type in a triad judgment task could lead people to pay less attention to surface level (irrelevant) features and pay more attention to deep (structural) features of information. SAMPLE: A sample of 313 participants recruited through Mechanical Turk participated in this study. METHOD: On each triad, participants were asked to evaluate which of two source scenarios went best with the target scenario...
June 19, 2017: Quarterly Journal of Experimental Psychology: QJEP
https://www.readbyqxmd.com/read/28626163/lower-serum-levels-of-mir-29c-3p-and-mir-19b-3p-as-biomarkers-for-alzheimer-s-disease
#6
Yuquan Wu, Juan Xu, Jing Xu, Jun Cheng, Demin Jiao, Chun Zhou, Yi Dai, Qingyong Chen
MicroRNAs (miRNAs) are short noncoding RNA that participate in posttranscriptional gene regulation. However, little is understood about the roles of miRNAs in Alzheimer's disease (AD). In this study, we used next-generation sequencing on RNA extracted from the serum samples of 20 AD patients and 20 controls, yielding a total of 72 miRNAs with significantly changed expression levels. Among these candidates, we selected 9 miRNAs with most significant alteration in disease, and validated their expression levels using RT-qPCR analysis on serum samples from 45 AD patients and 40 control subjects...
2017: Tohoku Journal of Experimental Medicine
https://www.readbyqxmd.com/read/28623886/epsilon-cp-using-deep-learning-to-combine-information-from-multiple-sources-for-protein-contact-prediction
#7
Kolja Stahl, Michael Schneider, Oliver Brock
BACKGROUND: Accurately predicted contacts allow to compute the 3D structure of a protein. Since the solution space of native residue-residue contact pairs is very large, it is necessary to leverage information to identify relevant regions of the solution space, i.e. correct contacts. Every additional source of information can contribute to narrowing down candidate regions. Therefore, recent methods combined evolutionary and sequence-based information as well as evolutionary and physicochemical information...
June 17, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28622676/training-dcnn-by-combining-max-margin-max-correlation-objectives-and-correntropy-loss-for-multilabel-image-classification
#8
Weiwei Shi, Yihong Gong, Xiaoyu Tao, Nanning Zheng
In this paper, we build a multilabel image classifier using a general deep convolutional neural network (DCNN). We propose a novel objective function that consists of three parts, i.e., max-margin objective, max-correlation objective, and correntropy loss. The max-margin objective explicitly enforces that the minimum score of positive labels must be larger than the maximum score of negative labels by a predefined margin, which not only improves accuracies of the multilabel classifier, but also eases the threshold determination...
June 13, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28622671/low-dose-ct-with-a-residual-encoder-decoder-convolutional-neural-network-red-cnn
#9
Hu Chen, Yi Zhang, Mannudeep K Kalra, Feng Lin, Yang Chen, Peixo Liao, Jiliu Zhou, Ge Wang
Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details...
June 13, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28618817/an-algorithm-to-increase-intelligibility-for-hearing-impaired-listeners-in-the-presence-of-a-competing-talker
#10
Eric W Healy, Masood Delfarah, Jordan L Vasko, Brittney L Carter, DeLiang Wang
Individuals with hearing impairment have particular difficulty perceptually segregating concurrent voices and understanding a talker in the presence of a competing voice. In contrast, individuals with normal hearing perform this task quite well. This listening situation represents a very different problem for both the human and machine listener, when compared to perceiving speech in other types of background noise. A machine learning algorithm is introduced here to address this listening situation. A deep neural network was trained to estimate the ideal ratio mask for a male target talker in the presence of a female competing talker...
June 2017: Journal of the Acoustical Society of America
https://www.readbyqxmd.com/read/28615794/deepinfer-open-source-deep-learning-deployment-toolkit-for-image-guided-therapy
#11
Alireza Mehrtash, Mehran Pesteie, Jorden Hetherington, Peter A Behringer, Tina Kapur, William M Wells, Robert Rohling, Andriy Fedorov, Purang Abolmaesumi
Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration...
February 11, 2017: Proceedings of SPIE
https://www.readbyqxmd.com/read/28615003/3d-deep-convolutional-neural-networks-for-amino-acid-environment-similarity-analysis
#12
Wen Torng, Russ B Altman
BACKGROUND: Central to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures...
June 14, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28613171/simultaneous-feature-and-dictionary-learning-for-image-set-based-face-recognition
#13
Jiwen Lu, Gang Wang, Jie Zhou
In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set based face recognition, where each training and testing example contains a set of face images which were captured from different variations of pose, illumination, expression, resolution and motion. While a variety of feature learning and dictionary learning methods have been proposed in recent years and some of them have been successfully applied to image set based face recognition, most of them learn features and dictionaries for facial image sets individually, which may not be powerful enough because some discriminative information for dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice versa...
June 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28613160/deep-canonical-time-warping-for-simultaneous-alignment-and-representation-learning-of-sequences
#14
George Trigeorgis, Mihalis Nicolaou, Stefanos Zafeiriou, Bjoern Schuller
Machine learning algorithms for the analysis of time-series often depend on the assumption that utilised data are temporally aligned. Any temporal discrepancies arising in the data is certain to lead to ill-generalisable models, which in turn fail to correctly capture properties of the task at hand. The temporal alignment of time-series is thus a crucial challenge manifesting in a multitude of applications. Nevertheless, the vast majority of algorithms oriented towards temporal alignment are either applied directly on the observation space or simply utilise linear projections - thus failing to capture complex, hierarchical non-linear representations that may prove beneficial, especially when dealing with multi-modal data (e...
June 8, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28611840/deep-learning-for-plant-identification-in-natural-environment
#15
Yu Sun, Yuan Liu, Guan Wang, Haiyan Zhang
Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28611579/stacked-autoencoders-for-the-p300-component-detection
#16
Lukáš Vařeka, Pavel Mautner
Novel neural network training methods (commonly referred to as deep learning) have emerged in recent years. Using a combination of unsupervised pre-training and subsequent fine-tuning, deep neural networks have become one of the most reliable classification methods. Since deep neural networks are especially powerful for high-dimensional and non-linear feature vectors, electroencephalography (EEG) and event-related potentials (ERPs) are one of the promising applications. Furthermore, to the authors' best knowledge, there are very few papers that study deep neural networks for EEG/ERP data...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28608992/student-and-recent-graduate-perspectives-on-radiological-imaging-instruction-during-basic-anatomy-courses
#17
Andrew W Phillips, Hunter Eason, Christopher M Straus
Recently, faculty at Pritzker School of Medicine, The University of Chicago, have made efforts to improve the depth of radiological anatomy knowledge that students have, but no insights exist as to student and resident opinions of how clinically helpful deep anatomical understanding is. A single-institution survey of second- and fourth-year medical students and postgraduate year 1-4 residents from 11 specialties, composed of five-point Likert questions, sample examination questions, and narrative response questions, was distributed in 2015...
June 13, 2017: Anatomical Sciences Education
https://www.readbyqxmd.com/read/28607637/the-perceived-stress-and-approach-to-learning-effects-on-academic-performance-among-sudanese-medical-students
#18
Hyder Osman Mirghni, Mohammed Adam Ahmed Elnour
BACKGROUND: There is an increasing awareness of the perceived stress and approach to learning effects on academic achievement. OBJECTIVE: This study aimed to assess the educational environment and approach to learning in clinical phase medical students. METHODS: This comparative cross-sectional study was conducted among fifty-nine clinical stage medical students at Omdurman Islamic University (Khartoum, Sudan) during the period from June to August 2016...
April 2017: Electronic Physician
https://www.readbyqxmd.com/read/28607456/an-advanced-deep-learning-approach-for-ki-67-stained-hotspot-detection-and-proliferation-rate-scoring-for-prognostic-evaluation-of-breast-cancer
#19
Monjoy Saha, Chandan Chakraborty, Indu Arun, Rosina Ahmed, Sanjoy Chatterjee
Being a non-histone protein, Ki-67 is one of the essential biomarkers for the immunohistochemical assessment of proliferation rate in breast cancer screening and grading. The Ki-67 signature is always sensitive to radiotherapy and chemotherapy. Due to random morphological, color and intensity variations of cell nuclei (immunopositive and immunonegative), manual/subjective assessment of Ki-67 scoring is error-prone and time-consuming. Hence, several machine learning approaches have been reported; nevertheless, none of them had worked on deep learning based hotspots detection and proliferation scoring...
June 12, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28606870/a-deep-learning-approach-for-predicting-the-quality-of-online-health-expert-question-answering-services
#20
Ze Hu, Zhan Zhang, Haiqin Yang, Qing Chen, Decheng Zuo
Recently, online health expert question-answering (HQA) services (systems) have attracted more and more health consumers to ask health-related questions everywhere at any time due to the convenience and effectiveness. However, the quality of answers in existing HQA systems varies in different situations. It is significant to provide effective tools to automatically determine the quality of the answers. Two main characteristics in HQA systems raise the difficulties of classification: 1) physicians' answers in an HQA system are usually written in short text, which yields the data sparsity issue; 2) HQA systems apply the quality control mechanism, which shield the wisdom of crowd...
June 9, 2017: Journal of Biomedical Informatics
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