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https://www.readbyqxmd.com/read/28819715/automatic-detection-of-hemorrhagic-pericardial-effusion-on-pmct-using-deep-learning-a-feasibility-study
#1
Lars C Ebert, Jakob Heimer, Wolf Schweitzer, Till Sieberth, Anja Leipner, Michael Thali, Garyfalia Ampanozi
Post mortem computed tomography (PMCT) can be used as a triage tool to better identify cases with a possibly non-natural cause of death, especially when high caseloads make it impossible to perform autopsies on all cases. Substantial data can be generated by modern medical scanners, especially in a forensic setting where the entire body is documented at high resolution. A solution for the resulting issues could be the use of deep learning techniques for automatic analysis of radiological images. In this article, we wanted to test the feasibility of such methods for forensic imaging by hypothesizing that deep learning methods can detect and segment a hemopericardium in PMCT...
August 18, 2017: Forensic Science, Medicine, and Pathology
https://www.readbyqxmd.com/read/28819110/development-and-assessment-of-a-lysophospholipid-based-deep-learning-model-to-discriminate-geographical-origins-of-white-rice
#2
Nguyen Phuoc Long, Dong Kyu Lim, Changyeun Mo, Giyoung Kim, Sung Won Kwon
Geographical origin determination of white rice has become the major issue of food industry. However, there is still lack of a high-throughput method for rapidly and reproducibly differentiating the geographical origins of commercial white rice. In this study, we developed a method that employed lipidomics and deep learning to discriminate white rice from Korea to China. A total of 126 white rice of 30 cultivars from different regions were utilized for the method development and validation. By using direct infusion-mass spectrometry-based targeted lipidomics, 17 lysoglycerophospholipids were simultaneously characterized within minutes per sample...
August 17, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28816677/prototype-incorporated-emotional-neural-network
#3
Oyebade K Oyedotun, Adnan Khashman
Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters...
August 15, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28816665/body-structure-aware-deep-crowd-counting
#4
Siyu Huang, Xi Li, Zhongfei Zhang, Fei Wu, Shenghua Gao, Rongrong Ji, Junwei Han
Crowd counting is a challenging task, mainly due to the severe occlusions among dense crowds. This work aims to take a broader view to address crowd counting from the perspective of semantic modelling. In essence, crowd counting is a task of pedestrian semantic analysis involving three key factors: pedestrians, heads, and their context structure. The information of different body parts is an important cue to help us judge whether there exists a person at a certain position. Existing methods usually perform crowd counting from the perspective of directly modelling the visual properties of either the whole body or the heads only, without explicitly capturing the composite body-part semantic structure information that is crucial for crowd counting...
August 14, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28816204/deep-band-modulated-phrase-perception-in-quiet-and-noise-in-individuals-with-auditory-neuropathy-spectrum-disorder-and-sensorineural-hearing-loss
#5
Hemanth Narayan Shetty, Vishal Kooknoor
CONTEXT: Deep band modulation (DBM) improves speech perception in individuals with learning disability and older adults, who had temporal impairment in them. However, it is unclear on perception of DBM phrases at quiet and noise conditions in individuals with auditory neuropathy spectrum disorder (ANSD) and sensorineural hearing loss (SNHL), as these individuals suffer from temporal impairment. AIM: The aim is to study the effect of DBM and noise on phrase perception in individuals with normal hearing, SNHL, and ANSD...
July 2017: Noise & Health
https://www.readbyqxmd.com/read/28815135/active-deep-learning-based-annotation-of-electroencephalography-reports-for-cohort-identification
#6
Ramon Maldonado, Travis R Goodwin, Sanda M Harabagiu
The annotation of a large corpus of Electroencephalography (EEG) reports is a crucial step in the development of an EEG-specific patient cohort retrieval system. The annotation of multiple types of EEG-specific medical concepts, along with their polarity and modality, is challenging, especially when automatically performed on Big Data. To address this challenge, we present a novel framework which combines the advantages of active and deep learning while producing annotations that capture a variety of attributes of medical concepts...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28815132/precision-diagnosis-of-melanoma-and-other-skin-lesions-from-digital-images
#7
Abhishek Bhattacharya, Albert Young, Andrew Wong, Simone Stalling, Maria Wei, Dexter Hadley
Melanoma will affect an estimated 73,000 new cases this year and result in 9,000 deaths, yet precise diagnosis remains a serious problem. Without early detection and preventative care, melanoma can quickly spread to become fatal (Stage IV 5-year survival rate is 20-10%) from a once localized skin lesion (Stage IA 5- year survival rate is 97%). There is no biomarker for melanoma in clinical use, and the current diagnostic criteria for skin lesions remains subjective and imprecise. Accurate diagnosis of melanoma relies on a histopathologic gold standard; thus, aggressive excision of melanocytic skin lesions has been the mainstay of treatment...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28815118/deep-learning-from-eeg-reports-for-inferring-underspecified-information
#8
Travis R Goodwin, Sanda M Harabagiu
Secondary use(1)of electronic health records (EHRs) often relies on the ability to automatically identify and extract information from EHRs. Unfortunately, EHRs are known to suffer from a variety of idiosyncrasies - most prevalently, they have been shown to often omit or underspecify information. Adapting traditional machine learning methods for inferring underspecified information relies on manually specifying features characterizing the specific information to recover (e.g. particular findings, test results, or physician's impressions)...
2017: AMIA Summits on Translational Science Proceedings
https://www.readbyqxmd.com/read/28814034/automated-assessment-of-symptom-severity-changes-during-deep-brain-stimulation-dbs-therapy-for-parkinson-s-disease
#9
Paolo Angeles, Yen Tai, Nicola Pavese, Samuel Wilson, Ravi Vaidyanathan
Deep brain stimulation (DBS) is currently being used as a treatment for symptoms of Parkinson's disease (PD). Tracking symptom severity progression and deciding the optimal stimulation parameters for people with PD is extremely difficult. This study presents a sensor system that can quantify the three cardinal motor symptoms of PD - rigidity, bradykinesia and tremor. The first phase of this study assesses whether data recorded from the system during physical examinations can be used to correlate to clinician's severity score using supervised machine learning (ML) models...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28812154/robotic-versus-laparoscopic-versus-open-colorectal-surgery-towards-defining-criteria-to-the-right-choice
#10
Matthew Zelhart, Andreas M Kaiser
OBJECTIVE: Analysis of various parameters related to the patient, the disease, and the needed surgical maneuvers to develop guidance for preoperative selection of the appropriate and the best approach for a given patient. Rapid advances in minimally invasive surgical technology are fascinating and challenging alike. It can be difficult for surgeons to keep up with new modalities that come on to the market place and to assess their true value, i.e., distinguish between fashionable trends versus scientific evidence...
August 15, 2017: Surgical Endoscopy
https://www.readbyqxmd.com/read/28810905/omni-polya-a-method-and-tool-for-accurate-recognition-of-poly-a-signals-in-human-genomic-dna
#11
Arturo Magana-Mora, Manal Kalkatawi, Vladimir B Bajic
BACKGROUND: Polyadenylation is a critical stage of RNA processing during the formation of mature mRNA, and is present in most of the known eukaryote protein-coding transcripts and many long non-coding RNAs. The correct identification of poly(A) signals (PAS) not only helps to elucidate the 3'-end genomic boundaries of a transcribed DNA region and gene regulatory mechanisms but also gives insight into the multiple transcript isoforms resulting from alternative PAS. Although progress has been made in the in-silico prediction of genomic signals, the recognition of PAS in DNA genomic sequences remains a challenge...
August 15, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28809698/deep-6-dof-tracking
#12
Mathieu Garon, Jean-Francois Lalonde
We present a temporal 6-DOF tracking method which leverages deep learning to achieve state-of-the-art performance on challenging datasets of real world capture. Our method is both more accurate and more robust to occlusions than the existing best performing approaches while maintaining real-time performance. To assess its efficacy, we evaluate our approach on several challenging RGBD sequences of real objects in a variety of conditions. Notably, we systematically evaluate robustness to occlusions through a series of sequences where the object to be tracked is increasingly occluded...
August 10, 2017: IEEE Transactions on Visualization and Computer Graphics
https://www.readbyqxmd.com/read/28809679/automatic-localization-of-the-needle-target-for-ultrasound-guided-epidural-injections
#13
Mehran Pesteie, Victoria Lessoway, Purang Abolmaesumi, Robert N Rohling
Accurate identification of the needle target is crucial for effective epidural anesthesia. Currently, epidural needle placement is administered by a manual technique, relying on the sense of feel, which has a significant failure rate. Moreover, misleading the needle may lead to inadequate anesthesia, post dural puncture headaches and other potential complications. Ultrasound offers guidance to the physician for identification of the needle target, but accurate interpretation and localization remain challenges...
August 11, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28809675/jointly-learning-deep-features-deformable-parts-occlusion-and-classification-for-pedestrian-detection
#14
Wanli Ouyang, Hui Zhou, Hongsheng Li, Quanquan Li, Junjie Yan, Xiaogang Wang
Feature extraction, deformation handling, occlusion handling, and classification are four important components in pedestrian detection. Existing methods learn or design these components either individually or sequentially. The interaction among these components is not yet well explored. This paper proposes that they should be jointly learned in order to maximize their strengths through cooperation. We formulate these four components into a joint deep learning framework and propose a new deep network architecture (Code available on www...
August 11, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28809673/heterogeneous-face-attribute-estimation-a-deep-multi-task-learning-approach
#15
Hu Han, Anil K Jain, Shiguang Shan, Xilin Chen
Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal and holistic vs. local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image...
August 10, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28807870/an-open-multi-vendor-multi-field-strength-brain-mr-dataset-and-analysis-of-publicly-available-skull-stripping-methods-agreement
#16
REVIEW
Roberto Souza, Oeslle Lucena, Julia Garrafa, David Gobbi, Marina Saluzzi, Simone Appenzeller, Letícia Rittner, Richard Frayne, Roberto Lotufo
This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquired on scanners from three vendors (Siemens, Philips and General Electric) at both 1.5 T and 3 T. CC-359 is comprised of 359 datasets, approximately 60 subjects per vendor and magnetic field strength. The dataset is approximately age and gender balanced, subject to the constraints of the available images...
August 11, 2017: NeuroImage
https://www.readbyqxmd.com/read/28801957/monitoring-and-depth-of-strategy-use-in-computer-based-learning-environments-for-science-and-history
#17
Victor M Deekens, Jeffrey A Greene, Nikki G Lobczowski
BACKGROUND: Self-regulated learning (SRL) models position metacognitive monitoring as central to SRL processing and predictive of student learning outcomes (Winne & Hadwin, 2008; Zimmerman, 2000). A body of research evidence also indicates that depth of strategy use, ranging from surface to deep processing, is predictive of learning performance. AIMS: In this study, we investigated the relationships among the frequency of metacognitive monitoring and the utilization of deep and surface-level strategies, and the connections between these SRL processes and learning outcomes across two academic domains, science and history...
August 12, 2017: British Journal of Educational Psychology
https://www.readbyqxmd.com/read/28800738/advances-in-closed-loop-deep-brain-stimulation-devices
#18
REVIEW
Mahboubeh Parastarfeizabadi, Abbas Z Kouzani
BACKGROUND: Millions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner. METHODS: This paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices...
August 11, 2017: Journal of Neuroengineering and Rehabilitation
https://www.readbyqxmd.com/read/28800442/pre-trained-convolutional-neural-networks-as-feature-extractors-for-tuberculosis-detection
#19
U K Lopes, J F Valiati
It is estimated that in 2015, approximately 1.8 million people infected by tuberculosis died, most of them in developing countries. Many of those deaths could have been prevented if the disease had been detected at an earlier stage, but the most advanced diagnosis methods are still cost prohibitive for mass adoption. One of the most popular tuberculosis diagnosis methods is the analysis of frontal thoracic radiographs; however, the impact of this method is diminished by the need for individual analysis of each radiography by properly trained radiologists...
August 4, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28798957/holographic-deep-learning-for-rapid-optical-screening-of-anthrax-spores
#20
YoungJu Jo, Sangjin Park, JaeHwang Jung, Jonghee Yoon, Hosung Joo, Min-Hyeok Kim, Suk-Jo Kang, Myung Chul Choi, Sang Yup Lee, YongKeun Park
Establishing early warning systems for anthrax attacks is crucial in biodefense. Despite numerous studies for decades, the limited sensitivity of conventional biochemical methods essentially requires preprocessing steps and thus has limitations to be used in realistic settings of biological warfare. We present an optical method for rapid and label-free screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells...
August 2017: Science Advances
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