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https://www.readbyqxmd.com/read/28526212/3d-deeply-supervised-network-for-automated-segmentation-of-volumetric-medical-images
#1
Qi Dou, Lequan Yu, Hao Chen, Yueming Jin, Xin Yang, Jing Qin, Pheng-Ann Heng
While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN...
May 8, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28524769/machine-learning-for-epigenetics-and-future-medical-applications
#2
Lawrence B Holder, M Muksitul Haque, Michael K Skinner
Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets...
May 19, 2017: Epigenetics: Official Journal of the DNA Methylation Society
https://www.readbyqxmd.com/read/28515787/a-computational-approach-for-the-functional-classification-of-the-epigenome
#3
Francesco Gandolfi, Anna Tramontano
BACKGROUND: In the last decade, advanced functional genomics approaches and deep sequencing have allowed large-scale mapping of histone modifications and other epigenetic marks, highlighting functional relationships between chromatin organization and genome function. Here, we propose a novel approach to explore functional interactions between different epigenetic modifications and extract combinatorial profiles that can be used to annotate the chromatin in a finite number of functional classes...
2017: Epigenetics & Chromatin
https://www.readbyqxmd.com/read/28515418/automatic-segmentation-of-kidneys-using-deep-learning-for-total-kidney-volume-quantification-in-autosomal-dominant-polycystic-kidney-disease
#4
Kanishka Sharma, Christian Rupprecht, Anna Caroli, Maria Carolina Aparicio, Andrea Remuzzi, Maximilian Baust, Nassir Navab
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most common inherited disorder of the kidneys. It is characterized by enlargement of the kidneys caused by progressive development of renal cysts, and thus assessment of total kidney volume (TKV) is crucial for studying disease progression in ADPKD. However, automatic segmentation of polycystic kidneys is a challenging task due to severe alteration in the morphology caused by non-uniform cyst formation and presence of adjacent liver cysts. In this study, an automated segmentation method based on deep learning has been proposed for TKV computation on computed tomography (CT) dataset of ADPKD patients exhibiting mild to moderate or severe renal insufficiency...
May 17, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28515009/transfer-learning-on-fused-multiparametric-mr-images-for-classifying-histopathological-subtypes-of-rhabdomyosarcoma
#5
Imon Banerjee, Alexis Crawley, Mythili Bhethanabotla, Heike E Daldrup-Link, Daniel L Rubin
This paper presents a deep-learning-based CADx for the differential diagnosis of embryonal (ERMS) and alveolar (ARMS) subtypes of rhabdomysarcoma (RMS) solely by analyzing multiparametric MR images. We formulated an automated pipeline that creates a comprehensive representation of tumor by performing a fusion of diffusion-weighted MR scans (DWI) and gadolinium chelate-enhanced T1-weighted MR scans (MRI). Finally, we adapted transfer learning approach where a pre-trained deep convolutional neural network has been fine-tuned based on the fused images for performing classification of the two RMS subtypes...
May 5, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28514151/deepppi-boosting-prediction-of-protein-protein-interactions-with-deep-neural-networks
#6
Xiuquan Du, Shiwei Sun, Changlin Hu, Yu Yao, Yuanting Yan, Yanping Zhang
The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many proteins variants statistically associated with human disease, nearly all such variants have unknown mechanisms, for example, protein-protein interactions (PPIs). In this study, we address this challenge using a recent machine learning advance-deep neural networks (DNNs). We aim at improving the performance of PPIs prediction and propose a method called DeepPPI (Deep neural networks for Protein-Protein Interactions prediction), which employs deep neural networks to effectively learn the representations of proteins from common protein descriptors...
May 17, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28513252/a-novel-model-based-on-fcm-lm-algorithm-for-prediction-of-protein-folding-rate
#7
Longlong Liu, Mingjiao Ma, Jing Cui
The prediction of protein folding rates is of paramount importance in describing the protein folding mechanism, which has broad applications in fields such as enzyme engineering and protein engineering. Therefore, predicting protein folding rates using the first-order of protein sequence, secondary structure and amino acid properties has become a very active research topic in recent years. This paper presents a new fuzzy cognitive map (FCM) model based on deep learning neural networks which uses data obtained from biological experiments to predict the protein folding rate...
April 25, 2017: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/28512513/using-a-concept-inventory-to-reveal-student-thinking-associated-with-common-misconceptions-about-antibiotic-resistance
#8
Ann M Stevens, Ann C Smith, Gili Marbach-Ad, Sarah A Balcom, John Buchner, Sandra L Daniel, Jeffrey J DeStefano, Najib M El-Sayed, Kenneth Frauwirth, Vincent T Lee, Kevin S McIver, Stephen B Melville, David M Mosser, David L Popham, Birgit E Scharf, Florian D Schubot, Richard W Seyler, Patricia Ann Shields, Wenxia Song, Daniel C Stein, Richard C Stewart, Katerina V Thompson, Zhaomin Yang, Stephanie A Yarwood
Misconceptions, also known as alternate conceptions, about key concepts often hinder the ability of students to learn new knowledge. Concept inventories (CIs) are designed to assess students' understanding of key concepts, especially those prone to misconceptions. Two-tiered CIs include prompts that ask students to explain the logic behind their answer choice. Such two-tiered CIs afford an opportunity for faculty to explore the student thinking behind the common misconceptions represented by their choice of a distractor...
April 2017: Journal of Microbiology & Biology Education: JMBE
https://www.readbyqxmd.com/read/28511125/the-many-facets-of-motor-learning-and-their-relevance-for-parkinson-s-disease
#9
REVIEW
Lucio Marinelli, Angelo Quartarone, Mark Hallett, Giuseppe Frazzitta, Maria Felice Ghilardi
The final goal of motor learning, a complex process that includes both implicit and explicit (or declarative) components, is the optimization and automatization of motor skills. Motor learning involves different neural networks and neurotransmitters systems depending on the type of task and on the stage of learning. After the first phase of acquisition, a motor skill goes through consolidation (i.e., becoming resistant to interference) and retention, processes in which sleep and long-term potentiation seem to play important roles...
April 9, 2017: Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology
https://www.readbyqxmd.com/read/28511066/deep-image-mining-for-diabetic-retinopathy-screening
#10
Gwenolé Quellec, Katia Charrière, Yassine Boudi, Béatrice Cochener, Mathieu Lamard
Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert knowledge about the target pathologies. However, deep learning algorithms, including the popular ConvNets, are black boxes: little is known about the local patterns analyzed by ConvNets to make a decision at the image level. A solution is proposed in this paper to create heatmaps showing which pixels in images play a role in the image-level predictions...
April 28, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28508345/deep-monocular-3d-reconstruction-for-assisted-navigation-in-bronchoscopy
#11
Marco Visentini-Scarzanella, Takamasa Sugiura, Toshimitsu Kaneko, Shinichiro Koto
PURPOSE: In bronchoschopy, computer vision systems for navigation assistance are an attractive low-cost solution to guide the endoscopist to target peripheral lesions for biopsy and histological analysis. We propose a decoupled deep learning architecture that projects input frames onto the domain of CT renderings, thus allowing offline training from patient-specific CT data. METHODS: A fully convolutional network architecture is implemented on GPU and tested on a phantom dataset involving 32 video sequences and [Formula: see text]60k frames with aligned ground truth and renderings, which is made available as the first public dataset for bronchoscopy navigation...
May 15, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28507724/deep-learning-and-3d-desi-imaging-reveal-the-hidden-metabolic-heterogeneity-of-cancer
#12
Paolo Inglese, James S McKenzie, Anna Mroz, James Kinross, Kirill Veselkov, Elaine Holmes, Zoltan Takats, Jeremy K Nicholson, Robert C Glen
Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure...
May 1, 2017: Chemical Science
https://www.readbyqxmd.com/read/28507695/ani-1-an-extensible-neural-network-potential-with-dft-accuracy-at-force-field-computational-cost
#13
J S Smith, O Isayev, A E Roitberg
Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation...
April 1, 2017: Chemical Science
https://www.readbyqxmd.com/read/28507325/corrigendum-characterisation-of-mental-health-conditions-in-social-media-using-informed-deep-learning
#14
George Gkotsis, Anika Oellrich, Sumithra Velupillai, Maria Liakata, Tim J P Hubbard, Richard J B Dobson, Rina Dutta
No abstract text is available yet for this article.
May 16, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28504939/correlation-preserving-photo-collage
#15
Lingjie Liu, Hongjie Zhang, Guangmei Jing, Yanwen Guo, Zhonggui Chen, Wenping Wang
A new method is presented for producing photo collages that preserve content correlation of photos. We use deep learning techniques to find correlation among given photos to facilitate their embedding on the canvas, and develop an efficient combinatorial optimization technique to make correlated photos stay close to each other. To make efficient use of canvas space, our method first extracts salient regions of photos and packs only these salient regions. We allow the salient regions to have arbitrary shapes, therefore yielding informative, yet more compact collages than by other similar collage methods based on salient regions...
May 12, 2017: IEEE Transactions on Visualization and Computer Graphics
https://www.readbyqxmd.com/read/28504932/generalizing-pooling-functions-in-cnns-mixed-gated-and-tree
#16
Chen-Yu Lee, Patrick Gallagher, Zhuowen Tu
In this paper, we seek to improve deep neural networks by generalizing the pooling operations that play a central role in the current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in: (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned...
May 12, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28501942/pulmonary-nodule-classification-with-deep-residual-networks
#17
Aiden Nibali, Zhen He, Dennis Wollersheim
PURPOSE  : Lung cancer has the highest death rate among all cancers in the USA. In this work we focus on improving the ability of computer-aided diagnosis (CAD) systems to predict the malignancy of nodules from cropped CT images of lung nodules. METHODS: We evaluate the effectiveness of very deep convolutional neural networks at the task of expert-level lung nodule malignancy classification. Using the state-of-the-art ResNet architecture as our basis, we explore the effect of curriculum learning, transfer learning, and varying network depth on the accuracy of malignancy classification...
May 13, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28500001/exemplar-based-image-and-video-stylization-using-fully-convolutional-semantic-features
#18
Feida Zhu, Zhicheng Yan, Jiajun Bu, Yizhou Yu
Color and tone stylization in images and videos strives to enhance unique themes with artistic color and tone adjustments. It has a broad range of applications from professional image postprocessing to photo sharing over social networks. Mainstream photo enhancement softwares, such as Adobe Lightroom and Instagram, provide users with predefined styles, which are often hand-crafted through a trial-and-error process. Such photo adjustment tools lack a semantic understanding of image contents and the resulting global color transform limits the range of artistic styles it can represent...
May 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28499443/erratum-to-deepcpg-accurate-prediction-of-single-cell-dna-methylation-states-using-deep-learning
#19
Christof Angermueller, Heather J Lee, Wolf Reik, Oliver Stegle
No abstract text is available yet for this article.
May 12, 2017: Genome Biology
https://www.readbyqxmd.com/read/28498711/projector-quantum-monte%C3%A2-carlo-method-for-nonlinear-wave-functions
#20
Lauretta R Schwarz, A Alavi, George H Booth
We reformulate the projected imaginary-time evolution of the full configuration interaction quantum Monte Carlo method in terms of a Lagrangian minimization. This naturally leads to the admission of polynomial complex wave function parametrizations, circumventing the exponential scaling of the approach. While previously these functions have traditionally inhabited the domain of variational Monte Carlo approaches, we consider recent developments for the identification of deep-learning neural networks to optimize this Lagrangian, which can be written as a modification of the propagator for the wave function dynamics...
April 28, 2017: Physical Review Letters
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