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https://www.readbyqxmd.com/read/28534800/a-deep-convolutional-neural-network-based-framework-for-automatic-fetal-facial-standard-plane-recognition
#1
Zhen Yu, Ee-Leng Tan, Dong Ni, Jing Qin, Siping Chen, Shenli Li, Baiying Lei, Tianfu Wang
Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-crafted features, but the recognition performance is still unsatisfactory due to the high intra-class variation of FFSPs and the high degree of visual similarity between FFSPs and other non-FFSPs...
May 17, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28534414/estimating-pm2-5-concentrations-in-the-conterminous-united-states-using-the-random-forest-approach
#2
Xuefei Hu, Jessica Hartmann Belle, Xia Meng, Avani Wildani, Lance Waller, Matthew Strickland, Yang Liu
To estimate PM2.5 concentrations, many parametric regression models have been developed, while non-parametric machine learning algorithms are used less often and national-scale models are rare. In this paper, we develop a random forest model incorporating Aerosol Optical Depth (AOD) data, meteorological fields, and land use variables to estimate daily 24-hour averaged ground level PM2.5 concentrations over the conterminous United States in 2011. Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability...
May 23, 2017: Environmental Science & Technology
https://www.readbyqxmd.com/read/28532357/visual-object-recognition-do-we-finally-know-more-now-than-we-did
#3
Isabel Gauthier, Michael J Tarr
How do we recognize objects despite changes in their appearance? The past three decades have been witness to intense debates regarding both whether objects are encoded invariantly with respect to viewing conditions and whether specialized, separable mechanisms are used for the recognition of different object categories. We argue that such dichotomous debates ask the wrong question. Much more important is the nature of object representations: What are features that enable invariance or differential processing between categories? Although the nature of object features is still an unanswered question, new methods for connecting data to models show significant potential for helping us to better understand neural codes for objects...
October 14, 2016: Annual Review of Vision Science
https://www.readbyqxmd.com/read/28530228/generic-decoding-of-seen-and-imagined-objects-using-hierarchical-visual-features
#4
Tomoyasu Horikawa, Yukiyasu Kamitani
Object recognition is a key function in both human and machine vision. While brain decoding of seen and imagined objects has been achieved, the prediction is limited to training examples. We present a decoding approach for arbitrary objects using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features, including those derived from a deep convolutional neural network, can be predicted from fMRI patterns, and that greater accuracy is achieved for low-/high-level features with lower-/higher-level visual areas, respectively...
May 22, 2017: Nature Communications
https://www.readbyqxmd.com/read/28526212/3d-deeply-supervised-network-for-automated-segmentation-of-volumetric-medical-images
#5
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/28515009/transfer-learning-on-fused-multiparametric-mr-images-for-classifying-histopathological-subtypes-of-rhabdomyosarcoma
#6
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/28508773/word-sense-disambiguation-of-medical-terms-via-recurrent-convolutional-neural-networks
#7
Sven Festag, Cord Spreckelsen
BACKGROUND: Tagging text data with codes representing biomedical concepts plays an important role in medical data management and analysis. A problem occurs if there are ambiguous words linked to several concepts. OBJECTIVES AND METHODS: This study aims at investigating word sense disambiguation based on word embedding and recurrent convolutional neural networks. The study focuses on terms mapped to multiple concepts of the Unified Medical Language System (UMLS)...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28506904/ordinal-convolutional-neural-networks-for-predicting-rdoc-positive-valence-psychiatric-symptom-severity-scores
#8
Anthony Rios, Ramakanth Kavuluru
BACKGROUND: The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes our methods, results, and experiences based on our participation in the second track of the shared task. OBJECTIVE: Classical methods of text classification usually fall into one of three problem types: binary, multi-class, and multi-label classification...
May 12, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28504949/cascaded-subpatch-networks-for-effective-cnns
#9
Xiaoheng Jiang, Yanwei Pang, Manli Sun, Xuelong Li
Conventional convolutional neural networks use either a linear or a nonlinear filter to extract features from an image patch (region) of spatial size Hx W (typically, H is small and is equal to W, e.g., H is 5 or 7 ). Generally, the size of the filter is equal to the size Hx W of the input patch. We argue that the representational ability of equal-size strategy is not strong enough. To overcome the drawback, we propose to use subpatch filter whose spatial size hx w is smaller than Hx W . The proposed subpatch filter consists of two subsequent filters...
May 12, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28501942/pulmonary-nodule-classification-with-deep-residual-networks
#10
Aiden Nibali, Zhen He, Dennis Wollersheim
PURPOSEĀ  : 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
#11
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/28495009/a-two-step-convolutional-neural-network-based-computer-aided-detection-scheme-for-automatically-segmenting-adipose-tissue-volume-depicting-on-ct-images
#12
Yunzhi Wang, Yuchen Qiu, Theresa Thai, Kathleen Moore, Hong Liu, Bin Zheng
Accurately assessment of adipose tissue volume inside a human body plays an important role in predicting disease or cancer risk, diagnosis and prognosis. In order to overcome limitation of using only one subjectively selected CT image slice to estimate size of fat areas, this study aims to develop and test a computer-aided detection (CAD) scheme based on deep learning technique to automatically segment subcutaneous fat areas (SFA) and visceral fat areas (VFA) depicting on volumetric CT images. A retrospectively collected CT image dataset was divided into two independent training and testing groups...
June 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28490744/precision-radiology-predicting-longevity-using-feature-engineering-and-deep-learning-methods-in-a-radiomics-framework
#13
Luke Oakden-Rayner, Gustavo Carneiro, Taryn Bessen, Jacinto C Nascimento, Andrew P Bradley, Lyle J Palmer
Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease...
May 10, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28489532/video-super-resolution-via-bidirectional-recurrent-convolutional-networks
#14
Yan Huang, Wei Wang, Liang Wang
Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR...
May 4, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28481301/convolutional-neural-network-based-human-detection-in-nighttime-images-using-visible-light-camera-sensors
#15
Jong Hyun Kim, Hyung Gil Hong, Kang Ryoung Park
Because intelligent surveillance systems have recently undergone rapid growth, research on accurately detecting humans in videos captured at a long distance is growing in importance. The existing research using visible light cameras has mainly focused on methods of human detection for daytime hours when there is outside light, but human detection during nighttime hours when there is no outside light is difficult. Thus, methods that employ additional near-infrared (NIR) illuminators and NIR cameras or thermal cameras have been used...
May 8, 2017: Sensors
https://www.readbyqxmd.com/read/28475069/deep-learning-for-automated-extraction-of-primary-sites-from-cancer-pathology-reports
#16
John Qiu, Hong-Jun Yoon, Paul A Fearn, Georgia D Tourassi
for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this study we investigated deep learning and a convolutional neural network (CNN), for extracting ICDO- 3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning...
May 3, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28475048/trunk-branch-ensemble-convolutional-neural-networks-for-video-based-face-recognition
#17
Changxing Ding, Dacheng Tao
Human faces in surveillance videos often suffer from severe image blur, dramatic pose variations, and occlusion. In this paper, we propose a comprehensive framework based on Convolutional Neural Networks (CNN) to overcome challenges in video-based face recognition (VFR). First, to learn blur-robust face representations, we artificially blur training data composed of clear still images to account for a shortfall in real-world video training data. Using training data composed of both still images and artificially blurred data, CNN is encouraged to learn blur-insensitive features automatically...
May 2, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28475047/retrieval-of-sentence-sequences-for-an-image-stream-via-coherence-recurrent-convolutional-networks
#18
Cesc Park, Youngjin Kim, Gunhee Kim
We propose an approach for retrieving a sequence of natural sentences for an image stream. Since general users often take a series of pictures on their experiences, much online visual information exists in the form of image streams, for which it would better take into consideration of the whole image stream to produce natural language descriptions. While almost all previous studies have dealt with the relation between a single image and a single natural sentence, our work extends both input and output dimension to a sequence of images and a sequence of sentences...
May 2, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28475046/convolutional-oriented-boundaries-from-image-segmentation-to-high-level-tasks
#19
Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Arbelaez, Luc Van Gool
We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). COB is computationally efficient, because it requires a single CNN forward pass for multi-scale contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets...
May 2, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28473055/automatic-feature-learning-using-multichannel-roi-based-on-deep-structured-algorithms-for-computerized-lung-cancer-diagnosis
#20
Wenqing Sun, Bin Zheng, Wei Qian
This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images...
April 13, 2017: Computers in Biology and Medicine
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