Search results for "Intelligence"
showing 10 items of 6959 documents
Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors.
2021
ObjectiveTo develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).MethodsPreoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating char…
The mixed capacitated general routing problem with turn penalties
2011
In this paper we deal with the mixed capacitated general routing problem with turn penalties. This problem generalizes many important arc and node routing problems, and it takes into account turn penalties and forbidden turns, which are crucial in many real-life applications, such as mail delivery, waste collection and street maintenance operations. Through a polynomial transformation of the considered problem into a Generalized Vehicle routing problem, we suggest a new approach for solving this new problem by transforming it into an Asymmetric Capacitated Vehicle routing problem. In this way, we can solve the new problem both optimally and heuristically using existing algorithms. A powerfu…
GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation
2018
In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac center-of-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results).…
A Robust and Simple Measure for Quality-Guided 2D Phase Unwrapping Algorithms
2016
Quality-based 2D phase unwrapping algorithms provide one of the best tradeoffs between speed and quality of results. Their robustness depends on a quality map, which is used to build a path that visits the most reliable pixels first. Unwrapping then proceeds along this path, delaying unwrapping of noisy and inconsistent areas until the end, so that the unwrapping errors remain local. We propose a novel quality measure that is consistent, technically sound, effective, fast to compute, and immune to the presence of a carrier signal. The new measure combines the benefits of both the quality-guided and the residue-based phase unwrapping approaches. The quality map is justified from the two diff…
Clinico-Immunological Status and Neurocognitive Function of Perinatally Acquired HIV-Positive Children on cART: A Cross-Sectional Correlational Study…
2020
Despite the undisputed benefits of combination antiretroviral therapy (cART), perinatally acquired human immunodeficiency virus (PHIV) children on treatment often present with a spectrum of neurological deficits known as HIV-associated neurocognitive impairment. Even higher CD4 cell count does not seem to prevent the development of neurocognitive impairment in children with PHIV. While CD4 cell count has shown to have the greatest prognostic value, its association with neurocognitive abilities remains to be clarified. This study aimed at determining the correlation between plasma CD4+ lymphocyte and neurocognitive function in children with PHIV on cART. In total, 152 purposively recruited h…
Cartoon filter via adaptive abstraction
2016
We propose a non-parametric methodology to realize abstraction images.The redundant wavelet "a trous" algorithm is applied for details detection.An multi-scale circular median filter is used as a smoothing filter.The proposed algorithm is simple and fast on low-cost entry-level hardware. Abstraction in computer graphics defines a procedure that discriminates the essential information that is worth keeping. Usually details, that correspond to higher frequency components, allow to distinguish otherwise similar images. Vice versa, low frequencies are related to the main information, which are larger structures. Contours themselves may also be identified by high frequencies and separate each pi…
Rethinking the sGLOH Descriptor
2018
sGLOH (shifting GLOH) is a histogram-based keypoint descriptor that can be associated to multiple quantized rotations of the keypoint patch without any recomputation. This property can be exploited to define the best distance between two descriptor vectors, thus avoiding computing the dominant orientation. In addition, sGLOH can reject incongruous correspondences by adding a global constraint on the rotations either as an a priori knowledge or based on the data. This paper thoroughly reconsiders sGLOH and improves it in terms of robustness, speed and descriptor dimension. The revised sGLOH embeds more quantized rotations, thus yielding more correct matches. A novel fast matching scheme is a…
Case-Sensitivity of Classifiers for WSD: Complex Systems Disambiguate Tough Words Better
2007
We present a novel method for improving disambiguation accuracy by building an optimal ensemble (OE) of systems where we predict the best available system for target word using a priori case factors (e.g. amount of training per sense). We report promising results of a series of best-system prediction tests (best prediction accuracy is 0.92) and show that complex/simple systems disambiguate tough/easy words better. The method provides the following benefits: (1) higher disambiguation accuracy for virtually any base systems (current best OE yields close to 2% accuracy gain over Senseval-3 state of the art) and (2) economical way of building more effective ensembles of all types (e.g. optimal,…
Near-infrared imaging and structured light ranging for automatic catheter insertion
2006
Vein localization and catheter insertion constitute the first and perhaps most important phase of many medical procedures. Currently, catheterization is performed manually by trained personnel. This process can prove problematic, however, depending upon various physiological factors of the patient. We present in this paper initial work for localizing surface veins via near-infrared (NIR) imaging and structured light ranging. The eventual goal of the system is to serve as the guidance for a fully automatic (i.e., robotic) catheterization device. Our proposed system is based upon near-infrared (NIR) imaging, which has previously been shown effective in enhancing the visibility of surface vein…
Maternal cell phone use during pregnancy and child cognition at age 5 years in 3 birth cohorts
2018
Background: There have been few studies of children's cognitive development in relation to mothers' cell phone use, and most were limited to outcomes at age 3 years or younger. We examined the relationship between maternal cell phone use during pregnancy and cognitive performance in 5-year old children. Methods: This study included data from 3 birth cohorts: the Danish National Birth Cohort (DNBC) (n = 1209), Spanish Environment and Childhood Project (INMA) (n = 1383), and Korean Mothers and Children's Environment Health Study (MOCEH) (n = 497). All cohorts collected information about maternal cell phone use during pregnancy and cognitive performance in children at age 5. We performed linea…