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Convolutional Neural Networks

N N Lebedeva, E D Karimova, S E Burkitbaev, V Yu Maltsev, A B Guekht
AIM: To reveal EEG patterns and activation of neural network changes during perception and realization of a simple motor act in healthy subjects and patients with affective disorders. MATERIAL AND METHODS: The patient group consisted of 15 people with affective disorders, the control group included 11 normals. As task subjects were asked to observe the movement of the experimenter hand (clenching of the hand), then submit this motion and then repeat it by themselves...
2018: Zhurnal Nevrologii i Psikhiatrii Imeni S.S. Korsakova
Alireza Mehrtash, Mohsen Ghafoorian, Guillaume Pernelle, Alireza Ziaei, Friso G Heslinga, Kemal Tuncali, Andriy Fedorov, Ron Kikinis, Clare M Tempany, William M Wells, Purang Abolmaesumi, Tina Kapur
Image-guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical research program in prostate biopsy. To the best of our knowledge, this is the first reported system for this clinical application, and also the first reported system that leverages deep neural networks for segmentation and localization of needles in MRI across biomedical applications...
October 18, 2018: IEEE Transactions on Medical Imaging
Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon, Shunxing Bao, Albert Assad, Tamara K Moyo, Michael R Savona, Richard G Abramson, Bennett A Landman
A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually traced images in one imaging modality (e.g., MRI) are able to train a segmentation network for another imaging modality (e.g., CT). In this paper, we propose an end-to-end synthetic segmentation network (SynSeg-Net) to train a segmentation network for a target imaging modality without having manual labels...
October 17, 2018: IEEE Transactions on Medical Imaging
Peng Tang, Xinggang Wang, Song Bai, Wei Shen, Xiang Bai, Wenyu Liu, Alan Loddon Yuille
Weakly Supervised Object Detection (WSOD), using only image-level annotations to train object detectors, is of growing importance in object recognition. In this paper, we propose a novel deep network for WSOD. Unlike previous networks that transfer the object detection problem to an image classification problem using Multiple Instance Learning (MIL), our strategy generates proposal clusters to learn refined instance classifiers by an iterative process. The proposals in the same cluster are spatially adjacent and associated with the same object...
October 16, 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Wenqi Ren, Jingang Zhang, Xiangyu Xu, Lin Ma, Xiaochun Cao, Gaofeng Meng, Wei Liu
Recent research have shown the potential of using convolutional neural networks (CNNs) to accomplish single image dehazing. In this work, we take one step further to explore the possibility of exploiting a network to perform haze removal for videos. Unlike single image dehazing, video based approaches can take advantage of the abundant information that exists across neighboring frames. In this work, assuming that a scene point yields highly correlated transmission values between adjacent video frames, we develop a deep learning solution for video dehazing, where a CNN is trained end-to-end to learn how to accumulate information across frames for transmission estimation...
October 15, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Risheng Liu, Long Ma, Yiyang Wang, Lei Zhang
Enhancing visual qualities of images plays very important roles in various vision and learning applications. In the past few years, both knowledge-driven maximum a posterior (MAP) with prior modelings and fully data-dependent convolutional neural network (CNN) techniques have been investigated to address specific enhancement tasks. In this paper, by exploiting the advantages of these two types of mechanisms within a complementary propagation perspective, we propose a unified framework, named deep prior ensemble (DPE), for solving various image enhancement tasks...
October 15, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Wenyuan Li, Jiayun Li, Karthik V Sarma, King Chung Ho, Shiwen Shen, Beatrice S Knudsen, Arkadiusz Gertych, Corey W Arnold
Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network (R-CNN) framework for multitask prediction using a Epithelial Network Head and a Grading Network Head. Compared to a single task model, our multi-task model can provide complementary contextual information, which contributes to better performance...
October 12, 2018: IEEE Transactions on Medical Imaging
Dipendra Jha, Saransh Singh, Reda Al-Bahrani, Wei-Keng Liao, Alok Choudhary, Marc De Graef, Ankit Agrawal
We present a deep learning approach to the indexing of electron backscatter diffraction (EBSD) patterns. We design and implement a deep convolutional neural network architecture to predict crystal orientation from the EBSD patterns. We design a differentiable approximation to the disorientation function between the predicted crystal orientation and the ground truth; the deep learning model optimizes for the mean disorientation error between the predicted crystal orientation and the ground truth using stochastic gradient descent...
October 2018: Microscopy and Microanalysis
Binbin Wang, Li Xiao, Yang Liu, Jing Wang, Beihong Liu, Tengyan Li, Xu Ma, Yi Zhao
There is a disparity between the increasing application of digital retinal imaging to neonatal ocular screening and slowly growing number of pediatric ophthalmologists. Assistant tools that can automatically detect ocular disorders may be needed. In present study, we develop a deep convolutional neural network (DCNN) for automated classification and grading of retinal hemorrhage. We used 48,996 digital fundus images from 3770 newborns with retinal hemorrhage of different severity (grade 1, 2, and 3) and normal controls from a large cross-sectional investigation in China...
October 17, 2018: Bioscience Reports
Titus Josef Brinker, Achim Hekler, Jochen Sven Utikal, Niels Grabe, Dirk Schadendorf, Joachim Klode, Carola Berking, Theresa Steeb, Alexander H Enk, Christof von Kalle
BACKGROUND: State-of-the-art classifiers based on convolutional neural networks (CNNs) were shown to classify images of skin cancer on par with dermatologists and could enable lifesaving and fast diagnoses, even outside the hospital via installation of apps on mobile devices. To our knowledge, at present there is no review of the current work in this research area. OBJECTIVE: This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs...
October 17, 2018: Journal of Medical Internet Research
Justyna P Zwolak, Sandesh S Kalantre, Xingyao Wu, Stephen Ragole, Jacob M Taylor
BACKGROUND: Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices...
2018: PloS One
Jose Dolz, Xiaopan Xu, Jérôme Rony, Jing Yuan, Yang Liu, Eric Granger, Christian Desrosiers, Xi Zhang, Ismail Ben Ayed, Hongbing Lu
PURPOSE: Precise segmentation of bladder walls and tumor regions is an essential step towards non-invasive identification of tumor stage and grade, which is critical for treatment decision and prognosis of patients with bladder cancer (BC). However, the automatic delineation of bladder walls and tumor in magnetic resonance images (MRI) is a challenging task, due to important bladder shape variations, strong intensity inhomogeneity in urine and very high variability across population, particularly on tumors appearance...
October 17, 2018: Medical Physics
Mehdi Alilou, Mahdi Orooji, Niha Beig, Prateek Prasanna, Prabhakar Rajiah, Christopher Donatelli, Vamsidhar Velcheti, Sagar Rakshit, Michael Yang, Frank Jacono, Robert Gilkeson, Philip Linden, Anant Madabhushi
Adenocarcinomas and active granulomas can both have a spiculated appearance on computed tomography (CT) and both are often fluorodeoxyglucose (FDG) avid on positron emission tomography (PET) scan, making them difficult to distinguish. Consequently, patients with benign granulomas are often subjected to invasive surgical biopsies or resections. In this study, quantitative vessel tortuosity (QVT), a novel CT imaging biomarker to distinguish between benign granulomas and adenocarcinomas on routine non-contrast lung CT scans is introduced...
October 16, 2018: Scientific Reports
Florian Dubost, Pinar Yilmaz, Hieab Adams, Gerda Bortsova, M Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease. As such, assessing the burden of PVS has promise as a brain imaging marker. Visual and manual scoring of PVS is a tedious and observer-dependent task. Automated methods would advance research into the etiology of PVS, could aid to assess what a "normal" burden is in aging, and could evaluate the potential of PVS as a biomarker of cerebral small vessel disease...
October 13, 2018: NeuroImage
Hong-Jie Dai, Jitendra Jonnagaddala
BACKGROUND AND OBJECTIVE: Efficiently capturing the severity of positive valence symptoms could aid in risk stratification for adverse outcomes among patients with psychiatric disorders and identify optimal treatment strategies for patient subgroups. Motivated by the success of convolutional neural networks (CNNs) in classification tasks, we studied the application of various CNN architectures and their performance in predicting the severity of positive valence symptoms in patients with psychiatric disorders based on initial psychiatric evaluation records...
2018: PloS One
Maximilian Treder, Jost Lennart Lauermann, Maged Alnawaiseh, Nicole Eter
PURPOSE: To evaluate a deep learning-based method to automatically detect graft detachment (GD) after Descemet membrane endothelial keratoplasty (DMEK) in anterior segment optical coherence tomography (AS-OCT). METHODS: In this study, a total of 1172 AS-OCT images (609: attached graft; 563: detached graft) were used to train and test a deep convolutional neural network to automatically detect GD after DMEK surgery in AS-OCT images. GD was defined as a not completely attached graft...
October 5, 2018: Cornea
Constance D Lehman, Adam Yala, Tal Schuster, Brian Dontchos, Manisha Bahl, Kyle Swanson, Regina Barzilay
Purpose To develop a deep learning (DL) algorithm to assess mammographic breast density. Materials and Methods In this retrospective study, a deep convolutional neural network was trained to assess Breast Imaging Reporting and Data System (BI-RADS) breast density based on the original interpretation by an experienced radiologist of 41 479 digital screening mammograms obtained in 27 684 women from January 2009 to May 2011. The resulting algorithm was tested on a held-out test set of 8677 mammograms in 5741 women...
October 16, 2018: Radiology
Jinfang Zheng, Xiaoli Zhang, Xunyi Zhao, Xiaoxue Tong, Xu Hong, Juan Xie, Shiyong Liu
RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow for that it needs to generate PSSM matrix as its feature. Herein, based on the protein feature of RBPPred and Convolutional Neural Network (CNN), we develop a deep learning model called Deep-RBPPred. With the balance and imbalance training set, we obtain Deep-RBPPred-balance and Deep-RBPPred-imbalance models...
October 15, 2018: Scientific Reports
Zhen Shen, Wenzheng Bao, De-Shuang Huang
It is well known that DNA sequence contains a certain amount of transcription factors (TF) binding sites, and only part of them are identified through biological experiments. However, these experiments are expensive and time-consuming. To overcome these problems, some computational methods, based on k-mer features or convolutional neural networks, have been proposed to identify TF binding sites from DNA sequences. Although these methods have good performance, the context information that relates to TF binding sites is still lacking...
October 15, 2018: Scientific Reports
Lukas Vogelsang, Sharon Gilad-Gutnick, Evan Ehrenberg, Albert Yonas, Sidney Diamond, Richard Held, Pawan Sinha
Children who are treated for congenital cataracts later exhibit impairments in configural face analysis. This has been explained in terms of a critical period for the acquisition of normal face processing. Here, we consider a more parsimonious account according to which deficits in configural analysis result from the abnormally high initial retinal acuity that children treated for cataracts experience, relative to typical newborns. According to this proposal, the initial period of low retinal acuity characteristic of normal visual development induces extended spatial processing in the cortex that is important for configural face judgments...
October 15, 2018: Proceedings of the National Academy of Sciences of the United States of America
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