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Convolutional Neural Networks

Yi-Pei Li, Kehang Han, Colin A Grambow, William H Green
Because collecting precise and accurate chemistry data is often challenging, chemistry datasets usually only span a small region of chemical space, which limits the performance and the scope of applicability of data-driven models. To address this issue, we integrated an active learning machine with automatic ab initio calculations to form a self-evolving model that can continuously adapt to new species appointed by the users. In the present work, we demonstrate the self-evolving concept by modeling the formation enthalpies of stable closed-shell polycyclic species calculated at the B3LYP/6-31G(2df,p) level of theory...
February 13, 2019: Journal of Physical Chemistry. A
Olli Öman, Teemu Mäkelä, Eero Salli, Sauli Savolainen, Marko Kangasniemi
BACKGROUND: The aim of this study was to investigate the feasibility of ischemic stroke detection from computed tomography angiography source images (CTA-SI) using three-dimensional convolutional neural networks. METHODS: CTA-SI of 60 patients with a suspected acute ischemic stroke of the middle cerebral artery were randomly selected for this study; 30 patients were used in the neural network training, and the subsequent testing was performed using the remaining 30 patients...
February 13, 2019: European radiology experimental
Md Zahangir Alom, Chris Yakopcic, Mst Shamima Nasrin, Tarek M Taha, Vijayan K Asari
The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. Breast cancer is one of the most common and dangerous cancers impacting women worldwide. In this paper, we have proposed a method for breast cancer classification with the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model...
February 12, 2019: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Halil Kilicoglu, Zeshan Peng, Shabnam Tafreshi, Tung Tran, Graciela Rosemblat, Jodi Schneider
Quantifying scientific impact of researchers and journals relies largely on citation counts, despite the acknowledged limitations of this approach. The need for more suitable alternatives has prompted research into developing advanced metrics, such as h-index and Relative Citation Ratio (RCR), as well as better citation categorization schemes to capture the various functions that citations serve in a publication. One such scheme involves citation sentiment: whether a reference paper is cited positively (agreement with the findings of the reference paper), negatively (disagreement), or neutrally...
February 9, 2019: Journal of Biomedical Informatics
Jim Clauwaert, Gerben Menschaert, Willem Waegeman
Annotation of gene expression in prokaryotes often finds itself corrected due to small variations of the annotated gene regions observed between different (sub)-species. It has become apparent that traditional sequence alignment algorithms, used for the curation of genomes, are not able to map the full complexity of the genomic landscape. We present DeepRibo, a novel neural network utilizing features extracted from ribosome profiling information and binding site sequence patterns that shows to be a precise tool for the delineation and annotation of expressed genes in prokaryotes...
February 8, 2019: Nucleic Acids Research
Yi-Yu Hsu, Mindy Clyne, Chih-Hsuan Wei, Muin J Khoury, Zhiyong Lu
Tracking scientific research publications on the evaluation, utility and implementation of genomic applications is critical for the translation of basic research to impact clinical and population health. In this work, we utilize state-of-the-art machine learning approaches to identify translational research in genomics beyond bench to bedside from the biomedical literature. We apply the convolutional neural networks (CNNs) and support vector machines (SVMs) to the bench/bedside article classification on the weekly manual annotation data of the Public Health Genomics Knowledge Base database...
January 1, 2019: Database: the Journal of Biological Databases and Curation
Dexiong Chen, Laurent Jacob, Julien Mairal
Motivation: The growing number of annotated biological sequences available makes it possible to learn genotype-phenotype relationships from data with increasingly high accuracy. When large quantities of labeled samples are available for training a model, convolutional neural networks can be used to predict the phenotype of unannotated sequences with good accuracy. Unfortunately, their performance with medium- or small-scale datasets is mitigated, which requires inventing new data-efficient approaches...
February 7, 2019: Bioinformatics
Georgia V Ionescu, Martin Fergie, Michael Berks, Elaine F Harkness, Johan Hulleman, Adam R Brentnall, Jack Cuzick, D Gareth Evans, Susan M Astley
Mammographic density is an important risk factor for breast cancer. In recent research, percentage density assessed visually using visual analogue scales (VAS) showed stronger risk prediction than existing automated density measures, suggesting readers may recognize relevant image features not yet captured by hand-crafted algorithms. With deep learning, it may be possible to encapsulate this knowledge in an automatic method. We have built convolutional neural networks (CNN) to predict density VAS scores from full-field digital mammograms...
July 2019: Journal of Medical Imaging
Connor W Coley, Wengong Jin, Luke Rogers, Timothy F Jamison, Tommi S Jaakkola, William H Green, Regina Barzilay, Klavs F Jensen
We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s). The prediction task is factored into two stages comparable to manual expert approaches: considering possible sites of reactivity and evaluating their relative likelihoods. By training on hundreds of thousands of reaction precedents covering a broad range of reaction types from the patent literature, the neural model makes informed predictions of chemical reactivity. The model predicts the major product correctly over 85% of the time requiring around 100 ms per example, a significantly higher accuracy than achieved by previous machine learning approaches, and performs on par with expert chemists with years of formal training...
January 14, 2019: Chemical Science
Jaewon Yang, Dookun Park, Grant T Gullberg, Youngho Seo
Dedicated brain positron emission tomography (PET) devices can provide higher-resolution images with much lower dose, compared to conventional whole-body PET systems, which is important to support PET neuroimaging particularly useful for the diagnosis of neurodegenerative diseases. However, when a dedicated brain PET scanner does not come with a combined CT or transmission source, there is no direct solution for accurate attenuation and scatter correction, both of which are critical for quantitative PET. To address this problem, we propose joint attenuation and scatter correction (ASC) in image space for non-corrected PET (PET<sub>NC</sub>) using deep convolutional neural networks (DCNN)...
February 11, 2019: Physics in Medicine and Biology
Yanlin Qi, Qi Li, Hamed Karimian, Di Liu
Increasing availability of data related to air quality from ground monitoring stations has provided the chance for data mining researchers to propose sophisticated models for predicting the concentrations of different air pollutants. In this paper, we proposed a hybrid model based on deep learning methods that integrates Graph Convolutional networks and Long Short-Term Memory networks (GC-LSTM) to model and forecast the spatiotemporal variation of PM2.5 concentrations. Specifically, historical observations on different stations are constructed as spatiotemporal graph series, and historical air quality variables, meteorological factors, spatial terms and temporal attributes are defined as graph signals...
February 1, 2019: Science of the Total Environment
Chan-Pang Kuok, Ming-Huwi Horng, Yu-Ming Liao, Nan-Haw Chow, Yung-Nien Sun
Tuberculosis (TB) remains the leading cause of morbidity and mortality from infectious disease in developing countries. The sputum smear microscopy remains the primary diagnostic laboratory test. However, microscopic examination is always time-consuming and tedious. Therefore, an effective computer-aided image identification system is needed to provide timely assistance in diagnosis. The current identification system usually suffers from complex color variations of the images, resulting in plentiful of false object detection...
February 11, 2019: Microscopy Research and Technique
Jeremiah W Sanders, Steven J Frank, Rajat J Kudchadker, Teresa L Bruno, Jingfei Ma
PURPOSE: To develop and evaluate a sliding-window convolutional neural network (CNN) for radioactive seed identification in MRI of the prostate after permanent implant brachytherapy. METHODS: Sixty-eight patients underwent prostate cancer low-dose-rate (LDR) brachytherapy using radioactive seeds stranded with positive contrast MR-signal seed markers and were scanned using a balanced steady-state free precession pulse sequence with and without an endorectal coil (ERC)...
February 8, 2019: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
Lianru Gao, Yiqun He, Xu Sun, Xiuping Jia, Bing Zhang
While ship detection using high-resolution optical satellite images plays an important role in various civilian fields-including maritime traffic survey and maritime rescue-it is a difficult task due to influences of the complex background, especially when ships are near to land. In current literatures, land masking is generally required before ship detection to avoid many false alarms on land. However, sea⁻land segmentation not only has the risk of segmentation errors, but also requires expertise to adjust parameters...
February 7, 2019: Sensors
Yi Lin, Xianlong Tan, Bo Yang, Kai Yang, Jianwei Zhang, Jing Yu
In order to obtain real-time controlling dynamics in air traffic system, a framework is proposed to introduce and process air traffic control (ATC) speech via radiotelephony communication. An automatic speech recognition (ASR) and controlling instruction understanding (CIU)-based pipeline is designed to convert the ATC speech into ATC related elements, i.e., controlling intent and parameters. A correction procedure is also proposed to improve the reliability of the information obtained by the proposed framework...
February 7, 2019: Sensors
Yu Zhang, Xinbo Gao, Lihuo He, Wen Lu, Ran He
Nowadays, video quality assessment (VQA) is essential to video compression technology applied to video transmission and storage. However, small-scale video quality databases with imbalanced samples and low-level feature representations for distorted videos impede the development of VQA methods. In this paper, we propose a full-reference (FR) VQA metric integrating transfer learning with a convolutional neural network (CNN). First, we imitate the feature-based transfer learning framework to transfer the distorted images as the related domain, which enriches the distorted samples...
February 6, 2019: IEEE Transactions on Neural Networks and Learning Systems
Rumeng Li, Baotian Hu, Feifan Liu, Weisong Liu, Francesca Cunningham, David D McManus, Hong Yu
BACKGROUND: Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences. As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance...
February 8, 2019: JMIR Medical Informatics
Fatemeh Zabihollahy, James A White, Eranga Ukwatta
PURPOSE: Accurate 3-dimensional (3D) segmentation of myocardial replacement fibrosis (i.e. scar) is emerging as a potentially valuable tool for risk stratification and procedural planning in patients with ischemic cardiomyopathy. The main purpose of this study was to develop a semi-automated method using a 3D convolutional neural network (CNN)-based for the segmentation of left ventricle (LV) myocardial scar from 3D late gadolinium enhancement magnetic resonance (LGE-MR) images. METHODS: Our proposed CNN is built upon several convolutional and pooling layers aimed at choosing appropriate features from LGE MR images to distinguish between myocardial scar and healthy tissues of the left ventricle...
February 8, 2019: Medical Physics
Xiangyuan Ma, Lubomir M Hadjiiski, Jun Wei, Heang-Ping Chan, Kenny H Cha, Richard H Cohan, Elaine M Caoili, Ravi Samala, Chuan Zhou, Yao Lu
OBJECTIVES: To develop a U-Net based deep learning approach (U-DL) for bladder segmentation in computed tomography urography (CTU) as a part of a computer-assisted bladder cancer detection and treatment response assessment pipeline. MATERIALS AND METHODS: A dataset of 173 cases including 81 cases in training/validation set (42 masses, 21 with wall thickening, 18 normal bladders), and 92 cases in the test set (43 masses, 36 with wall thickening, 13 normal bladders) were used with Institutional Review Board (IRB) approval...
February 8, 2019: Medical Physics
Noorul Wahab, Asifullah Khan, Yeon Soo Lee
Segmentation and detection of mitotic nuclei is a challenging task. To address this problem, a Transfer Learning based fast and accurate system is proposed. To give the classifier a balanced dataset, this work exploits the concept of Transfer Learning by first using a pre-trained convolutional neural network (CNN) for segmentation, and then another Hybrid-CNN (with Weights Transfer and custom layers) for classification of mitoses. First, mitotic nuclei are automatically annotated, based on the ground truth centroids...
February 5, 2019: Microscopy
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