83 results match your criteria deeply supervised

Community perspectives on supervised consumption sites: Insights from four U.S. counties deeply affected by opioids.

J Subst Abuse Treat 2021 Apr 20;131:108397. Epub 2021 Apr 20.

Drug Policy Research Center, RAND Corporation, 1776 Main St., Santa Monica, CA 90407, United States of America.

Background: To address the overdose crisis in the United States, expert groups have been nearly unanimous in calls for increasing access to evidence-based treatment and overdose reversal drugs. In some places there have also been calls for implementing supervised consumption sites (SCSs). Some cities-primarily in coastal urban areas-have explored the feasibility and acceptability of introducing them. Read More

View Article and Full-Text PDF

Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis.

Wei Sun Tianfu Wu

IEEE Trans Pattern Anal Mach Intell 2021 May 10;PP. Epub 2021 May 10.

With the remarkable recent progress on learning deep generative models, it becomes increasingly interesting to develop models for controllable image synthesis from reconfigurable structured inputs. This paper focuses on a recently emerged task, layout-to-image, whose goal is to learn generative models for synthesizing photo-realistic images from a spatial layout (i.e. Read More

View Article and Full-Text PDF

Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks.

Front Oncol 2021 4;11:588010. Epub 2021 Mar 4.

Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.

Background And Purpose: It is extremely important to predict the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) before surgery, which is a key predictor of recurrence and helps determine the treatment strategy before liver resection or liver transplantation. In this study, we demonstrate that a deep learning approach based on contrast-enhanced MR and 3D convolutional neural networks (CNN) can be applied to better predict MVI in HCC patients.

Materials And Methods: This retrospective study included 114 consecutive patients who were surgically resected from October 2012 to October 2018 with 117 histologically confirmed HCC. Read More

View Article and Full-Text PDF

A Deeply Supervised Convolutional Neural Network for Pavement Crack Detection With Multiscale Feature Fusion.

IEEE Trans Neural Netw Learn Syst 2021 Mar 15;PP. Epub 2021 Mar 15.

Automatic crack detection is vital for efficient and economical road maintenance. With the explosive development of convolutional neural networks (CNNs), recent crack detection methods are mostly based on CNNs. In this article, we propose a deeply supervised convolutional neural network for crack detection via a novel multiscale convolutional feature fusion module. Read More

View Article and Full-Text PDF

Hybrid Regularization of Diffusion Process for Visual Re-Ranking.

IEEE Trans Image Process 2021 17;30:3705-3719. Epub 2021 Mar 17.

To improve the retrieval result obtained from a pairwise dissimilarity, many variants of diffusion process have been applied in visual re-ranking. In the framework of diffusion process, various contextual similarities can be obtained by solving an optimization problem, and the objective function consists of a smoothness constraint and a fitting constraint. And many improvements on the smoothness constraint have been made to reveal the underlying manifold structure. Read More

View Article and Full-Text PDF

Learning From Architectural Redundancy: Enhanced Deep Supervision in Deep Multipath Encoder-Decoder Networks.

IEEE Trans Neural Netw Learn Syst 2021 Feb 15;PP. Epub 2021 Feb 15.

Deep encoder-decoders are the model of choice for pixel-level estimation due to their redundant deep architectures. Yet they still suffer from the vanishing supervision information issue that affects convergence because of their overly deep architectures. In this work, we propose and theoretically derive an enhanced deep supervision (EDS) method which improves on conventional deep supervision (DS) by incorporating variance minimization into the optimization. Read More

View Article and Full-Text PDF
February 2021

DNA: Deeply Supervised Nonlinear Aggregation for Salient Object Detection.

IEEE Trans Cybern 2021 Feb 2;PP. Epub 2021 Feb 2.

Recent progress on salient object detection mainly aims at exploiting how to effectively integrate multiscale convolutional features in convolutional neural networks (CNNs). Many popular methods impose deep supervision to perform side-output predictions that are linearly aggregated for final saliency prediction. In this article, we theoretically and experimentally demonstrate that linear aggregation of side-output predictions is suboptimal, and it only makes limited use of the side-output information obtained by deep supervision. Read More

View Article and Full-Text PDF
February 2021

Fetal Ultrasound Image Segmentation for Automatic Head Circumference Biometry Using Deeply Supervised Attention-Gated V-Net.

J Digit Imaging 2021 Feb 22;34(1):134-148. Epub 2021 Jan 22.

Department of Biomedical Engineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing, China.

Automatic computerized segmentation of fetal head from ultrasound images and head circumference (HC) biometric measurement is still challenging, due to the inherent characteristics of fetal ultrasound images at different semesters of pregnancy. In this paper, we proposed a new deep learning method for automatic fetal ultrasound image segmentation and HC biometry: deeply supervised attention-gated (DAG) V-Net, which incorporated the attention mechanism and deep supervision strategy into V-Net models. In addition, multi-scale loss function was introduced for deep supervision. Read More

View Article and Full-Text PDF
February 2021

Automatic classification of mice vocalizations using Machine Learning techniques and Convolutional Neural Networks.

PLoS One 2021 19;16(1):e0244636. Epub 2021 Jan 19.

Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.

Ultrasonic vocalizations (USVs) analysis is a well-recognized tool to investigate animal communication. It can be used for behavioral phenotyping of murine models of different disorders. The USVs are usually recorded with a microphone sensitive to ultrasound frequencies and they are analyzed by specific software. Read More

View Article and Full-Text PDF

DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation.

IEEE J Biomed Health Inform 2021 Jan 18;PP. Epub 2021 Jan 18.

Image segmentation is one of the most essential biomedical image processing problems for different imaging modalities, including microscopy and X-ray in the Internet-of-Medical-Things (IoMT) domain. However, annotating biomedical images is knowledge-driven, time-consuming, and labor-intensive, making it difficult to obtain abundant labels with limited costs. Active learning strategies come into ease the burden of human annotation, which queries only a subset of training data for annotation. Read More

View Article and Full-Text PDF
January 2021

Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification.

Med Phys 2021 Feb 30;48(2):733-744. Epub 2020 Dec 30.

Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, 9713 AV, Groningen, The Netherlands.

Purpose: Early detection of lung cancer is of importance since it can increase patients' chances of survival. To detect nodules accurately during screening, radiologists would commonly take the axial, coronal, and sagittal planes into account, rather than solely the axial plane in clinical evaluation. Inspired by clinical work, the paper aims to develop an accurate deep learning framework for nodule detection by a combination of multiple planes. Read More

View Article and Full-Text PDF
February 2021

Deeply-supervised density regression for automatic cell counting in microscopy images.

Med Image Anal 2021 02 11;68:101892. Epub 2020 Nov 11.

Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA; Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA; Carle Cancer Center, Carle Foundation Hospital, Urbana, IL 61801 USA. Electronic address:

Accurately counting the number of cells in microscopy images is required in many medical diagnosis and biological studies. This task is tedious, time-consuming, and prone to subjective errors. However, designing automatic counting methods remains challenging due to low image contrast, complex background, large variance in cell shapes and counts, and significant cell occlusions in two-dimensional microscopy images. Read More

View Article and Full-Text PDF
February 2021

Impact of a Pitanga Leaf Extract to Prevent Lipid Oxidation Processes during Shelf Life of Packaged Pork Burgers: An Untargeted Metabolomic Approach.

Foods 2020 Nov 15;9(11). Epub 2020 Nov 15.

Department for Sustainable Food Process (DiSTAS), Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy.

In this work, the comprehensive metabolomic changes in pork burgers treated with different antioxidants, namely, (a) a control without antioxidants, (b) 200 mg/kg butylated hydroxytoluene (BHT), and (c) 250 mg/kg pitanga leaf extract (PLE, from L.), each one packaged under modified atmosphere (80% O and 20% CO) for 18 days storage at 2 ± 1 °C, were deeply studied. In particular, untargeted metabolomics was used to evaluate the impact of the antioxidant extracts on meat quality. Read More

View Article and Full-Text PDF
November 2020

Impact of 2 different aerobic periodization training protocols on left ventricular function in patients with stable coronary artery disease: an exploratory study.

Appl Physiol Nutr Metab 2021 May 27;46(5):436-442. Epub 2020 Oct 27.

Department of Medicine, Faculty of Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada.

We compared the impacts of linear (LP) and nonlinear (NLP) aerobic training periodizations on left ventricular (LV) function and geometry in coronary artery disease (CAD) patients. Thirty-nine CAD patients were randomized to either a 3-month isoenergetic supervised LP or NLP. All underwent standard echocardiography with assessment of 3D LV ejection fraction (LVEF), diastolic function, strain (global longitudinal, radial, and circumferential), and strain rate at baseline and study end. Read More

View Article and Full-Text PDF

Deeply Supervised Active Learning for Finger Bones Segmentation.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:1620-1623

Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segmentation. The proposed architecture is fine-tuned in an iterative and incremental learning manner. Read More

View Article and Full-Text PDF

Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics.

Sensors (Basel) 2020 Aug 12;20(16). Epub 2020 Aug 12.

Indra, Digital Labs, Av. de Bruselas, 35, Alcobendas, 28108 Madrid, Spain.

Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. Read More

View Article and Full-Text PDF

DNF-Net: a Deep Normal Filtering Network for Mesh Denoising.

IEEE Trans Vis Comput Graph 2020 Jun 11;PP. Epub 2020 Jun 11.

This paper presents a deep normal filtering network, called DNF-Net, for mesh denoising. To better capture local geometry, our network processes the mesh in terms of local patches extracted from the mesh. Overall, DNF-Net is an end-to-end network that takes patches of facet normals as inputs and directly outputs the corresponding denoised facet normals of the patches. Read More

View Article and Full-Text PDF

Peer-teaching at the University of Rwanda - a qualitative study based on self-determination theory.

BMC Med Educ 2020 Jul 20;20(1):230. Epub 2020 Jul 20.

Department of Paediatrics, University Teaching Hospital of Kigali (CHUK), Kigali, Rwanda.

Background: Peer-teaching is an educational format in which one student teaches one, or more, fellow students. Self-determination theory suggests that intrinsic motivation increases with the enhancement of autonomy, competence and relatedness.

Aims: This qualitative study sought to explore and better understand the lived experiences, attitudes and perceptions of medical students as peer-teachers at the University of Rwanda when participating in a peer-learning intervention in the pediatric department. Read More

View Article and Full-Text PDF

Rubik's Cube+: A self-supervised feature learning framework for 3D medical image analysis.

Med Image Anal 2020 08 6;64:101746. Epub 2020 Jun 6.

Tencent Jarvis Lab, Shenzhen, China.

Due to the development of deep learning, an increasing number of research works have been proposed to establish automated analysis systems for 3D volumetric medical data to improve the quality of patient care. However, it is challenging to obtain a large number of annotated 3D medical data needed to train a neural network well, as such manual annotation by physicians is time consuming and laborious. Self-supervised learning is one of the potential solutions to mitigate the strong requirement of data annotation by deeply exploiting raw data information. Read More

View Article and Full-Text PDF

Automatic multi-catheter detection using deeply supervised convolutional neural network in MRI-guided HDR prostate brachytherapy.

Med Phys 2020 Sep 15;47(9):4115-4124. Epub 2020 Jun 15.

Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30332, USA.

Purpose: High-dose-rate (HDR) brachytherapy is an established technique to be used as monotherapy option or focal boost in conjunction with external beam radiation therapy (EBRT) for treating prostate cancer. Radiation source path reconstruction is a critical procedure in HDR treatment planning. Manually identifying the source path is labor intensive and time inefficient. Read More

View Article and Full-Text PDF
September 2020

Bio-Inspired Techniques in a Fully Digital Approach for Lifelong Learning.

Front Neurosci 2020 30;14:379. Epub 2020 Apr 30.

Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milan, Italy.

Lifelong learning has deeply underpinned the resilience of biological organisms respect to a constantly changing environment. This flexibility has allowed the evolution of parallel-distributed systems able to merge past information with new stimulus for accurate and efficient brain-computation. Nowadays, there is a strong attempt to reproduce such intelligent systems in standard artificial neural networks (ANNs). Read More

View Article and Full-Text PDF

Segmentation of finger tendon and synovial sheath in ultrasound image using deep convolutional neural network.

Biomed Eng Online 2020 Apr 22;19(1):24. Epub 2020 Apr 22.

Department of Computer Science and Information Engineering, 1 University Road, Tainan, 701, Taiwan.

Background: Trigger finger is a common hand disease, which is caused by a mismatch in diameter between the tendon and the pulley. Ultrasound images are typically used to diagnose this disease, which are also used to guide surgical treatment. However, background noise and unclear tissue boundaries in the images increase the difficulty of the process. Read More

View Article and Full-Text PDF

Deep Recognition of Vanishing-Point-Constrained Building Planes in Urban Street Views.

IEEE Trans Image Process 2020 Apr 15. Epub 2020 Apr 15.

This paper presents a new approach to recognizing vanishing-point-constrained building planes from a single image of street view. We first design a novel convolutional neural network (CNN) architecture that generates geometric segmentation of per-pixel orientations from a single street-view image. The network combines two-stream features of general visual cues and surface normals in gated convolution layers, and employs a deeply supervised loss that encapsulates multi-scale convolutional features. Read More

View Article and Full-Text PDF

Deeply self-supervised contour embedded neural network applied to liver segmentation.

Comput Methods Programs Biomed 2020 Aug 15;192:105447. Epub 2020 Mar 15.

School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea. Electronic address:

Objective: Herein, a neural network-based liver segmentation algorithm is proposed, and its performance was evaluated using abdominal computed tomography (CT) images.

Methods: A fully convolutional network was developed to overcome the volumetric image segmentation problem. To guide a neural network to accurately delineate a target liver object, the network was deeply supervised by applying the adaptive self-supervision scheme to derive the essential contour, which acted as a complement with the global shape. Read More

View Article and Full-Text PDF

Intrapapillary capillary loop classification in magnification endoscopy: open dataset and baseline methodology.

Int J Comput Assist Radiol Surg 2020 Apr 12;15(4):651-659. Epub 2020 Mar 12.

School of Biomedical Engineering and Imaging Science, KCL, London, UK.

Purpose: Early squamous cell neoplasia (ESCN) in the oesophagus is a highly treatable condition. Lesions confined to the mucosal layer can be curatively treated endoscopically. We build a computer-assisted detection system that can classify still images or video frames as normal or abnormal with high diagnostic accuracy. Read More

View Article and Full-Text PDF

Multi-needle Localization with Attention U-Net in US-guided HDR Prostate Brachytherapy.

Med Phys 2020 Jul 3;47(7):2735-2745. Epub 2020 Apr 3.

Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA.

Purpose: Ultrasound (US)-guided high dose rate (HDR) prostate brachytherapy requests the clinicians to place HDR needles (catheters) into the prostate gland under transrectal US (TRUS) guidance in the operating room. The quality of the subsequent radiation treatment plan is largely dictated by the needle placements, which varies upon the experience level of the clinicians and the procedure protocols. Real-time plan dose distribution, if available, could be a vital tool to provide more subjective assessment of the needle placements, hence potentially improving the radiation plan quality and the treatment outcome. Read More

View Article and Full-Text PDF

Deeply Supervised Discriminative Learning for Adversarial Defense.

IEEE Trans Pattern Anal Mach Intell 2020 Mar 5. Epub 2020 Mar 5.

Deep neural networks can easily be fooled by an adversary using minuscule perturbations to input images. The existing defense techniques suffer greatly under white-box attack settings, where an adversary has full knowledge about the network and can iterate several times to find strong perturbations. We observe that the main reason for the existence of such vulnerabilities is the close proximity of different class samples in the learned feature space of deep models. Read More

View Article and Full-Text PDF

Integrative Analysis Identifies Candidate Tumor Microenvironment and Intracellular Signaling Pathways that Define Tumor Heterogeneity in NF1.

Genes (Basel) 2020 02 21;11(2). Epub 2020 Feb 21.

Computational Oncology, Sage Bionetworks, Seattle, WA 98121, USA.

Neurofibromatosis type 1 (NF1) is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, whereas 40-60% of patients develop plexiform neurofibromas (pNFs), which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). Read More

View Article and Full-Text PDF
February 2020

Deep Multiphase Level Set for Scene Parsing.

IEEE Trans Image Process 2020 Feb 19. Epub 2020 Feb 19.

Recently, Fully Convolutional Network (FCN) seems to be the go-to architecture for image segmentation, including semantic scene parsing. However, it is difficult for a generic FCN to predict semantic labels around the object boundaries, thus FCN-based methods usually produce parsing results with inaccurate boundaries. Meanwhile, many works have demonstrate that level set based active contours are superior to the boundary estimation in sub-pixel accuracy. Read More

View Article and Full-Text PDF
February 2020