Search results for "lcsh:Physics"
showing 10 items of 778 documents
Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
2021
Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardwar…
Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks
2021
[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was tra…
Deep Convolutional Neural Network for HEp-2 fluorescence intensity classification
2019
Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the …
An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification
2019
The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentati…
Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
2019
In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of …
The Elephant in the Machine: Proposing a New Metric of Data Reliability and its Application to a Medical Case to Assess Classification Reliability
2020
In this paper, we present and discuss a novel reliability metric to quantify the extent a ground truth, generated in multi-rater settings, as a reliable basis for the training and validation of machine learning predictive models. To define this metric, three dimensions are taken into account: agreement (that is, how much a group of raters mutually agree on a single case)
Biomechanical analysis of two types of osseointegrated transfemoral prosthesis
2020
In the last two decades, osseointegrated prostheses have been shown to be a good alternative for lower limb amputees experiencing complications in using a traditional socket-type prosthesis
Free-standing 2D metals from binary metal alloys
2020
Recent experiment demonstrated the formation of free-standing Au monolayers by exposing Au-Ag alloy to electron beam irradiation. Inspired by this discovery, we used semi-empirical effective medium theory simulations to investigate monolayer formation in 30 different binary metal alloys composed of late d-series metals Ni, Cu, Pd, Ag, Pt, and Au. In qualitative agreement with the experiment, we find that the beam energy required to dealloy Ag atoms from Au-Ag alloy is smaller than the energy required to break the dealloyed Au monolayer. Our simulations suggest that similar method could also be used to form Au monolayers from Au-Cu alloy and Pt monolayers from Pt-Cu, Pt-Ni, and Pt-Pd alloys.
Ferromagnetic layer thickness dependence of the Dzyaloshinskii-Moriya interaction and spin-orbit torques in Pt\Co\AlO x
2017
We report the thickness dependence of the Dzyaloshinskii-Moriya interaction (DMI) and spin-orbit torques (SOTs) in Pt\Co(t)\AlOx, studied by current-induced domain wall (DW) motion and second-harmonic experiments. From the DW motion study, a monotonous decay of the effective DMI strength with increasing Co thickness is observed, in agreement with a DMI originating from the Pt\Co interface. The study of the ferromagnetic layer thickness dependence of spin-orbit torques reveals a more complex behavior. The observed thickness dependence suggests the spin-Hall effect in Pt as the main origin of the SOTs, with the measured SOT-fields amplitudes resulting from the interplay between the varying th…
Assessment of Arundo donax Fibers for Oil Spill Recovery Applications
2019
In the last years, natural fibers are increasingly investigated as an oil recovery system in order to overcome the oil spillage phenomena, thus preserving environment and aquatic life. In particular, lignocellulose-based fibers have recently been employed with promising results. In such a context, the aim of this paper is to assess the oil sorption capability of natural fibers extracted from the stem of the giant reed Arundo donax L., a perennial rhizomatous grass belonging to the Poaceae family that grows naturally all around the world thanks to its ability to tolerate different climatic conditions. Sorption tests in several pollutants and water as a reference were carried out. The fibers …