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Deep convolutional neural network

Sara Rabhi, Jérémie Jakubowicz, Marie-Helene Metzger
OBJECTIVE:  The objective of this article was to compare the performances of health care-associated infection (HAI) detection between deep learning and conventional machine learning (ML) methods in French medical reports. METHODS:  The corpus consisted in different types of medical reports (discharge summaries, surgery reports, consultation reports, etc.). A total of 1,531 medical text documents were extracted and deidentified in three French university hospitals...
March 15, 2019: Methods of Information in Medicine
Dimitrios Ataloglou, Anastasios Dimou, Dimitrios Zarpalas, Petros Daras
Automatic segmentation of the hippocampus from 3D magnetic resonance imaging mostly relied on multi-atlas registration methods. In this work, we exploit recent advances in deep learning to design and implement a fully automatic segmentation method, offering both superior accuracy and fast result. The proposed method is based on deep Convolutional Neural Networks (CNNs) and incorporates distinct segmentation and error correction steps. Segmentation masks are produced by an ensemble of three independent models, operating with orthogonal slices of the input volume, while erroneous labels are subsequently corrected by a combination of Replace and Refine networks...
March 15, 2019: Neuroinformatics
Jeong Hoon Lee, Eun Ju Ha, Ju Han Kim
PURPOSE: To develop a deep learning-based computer-aided diagnosis (CAD) system for use in the CT diagnosis of cervical lymph node metastasis (LNM) in patients with thyroid cancer. METHODS: A total of 995 axial CT images that included benign (n = 647) and malignant (n = 348) lymph nodes were collected from 202 patients with thyroid cancer who underwent CT for surgical planning between July 2017 and January 2018. The datasets were randomly split into training (79...
March 15, 2019: European Radiology
Isadora Cardoso, Eliana Almeida, Hector Allende-Cid, Alejandro C Frery, Rangaraj M Rangayyan, Paulo M Azevedo-Marques, Heitor S Ramos
Computational Intelligence Re-meets Medical Image Processing A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration BACKGROUND:  Diffuse lung diseases (DLDs) are a diverse group of pulmonary disorders, characterized by inflammation of lung tissue, which may lead to permanent loss of the ability to breathe and death. Distinguishing among these diseases is challenging to physicians due their wide variety and unknown causes. Computer-aided diagnosis (CAD) is a useful approach to improve diagnostic accuracy, by combining information provided by experts with Machine Learning (ML) methods...
November 2018: Methods of Information in Medicine
Guillaume Pagès, Benoit Charmettant, Sergei Grudinin
MOTIVATION: Protein model quality assessment (QA) is a crucial and yet open problem in structural bioinformatics. The current best methods for single-model QA typically combine results from different approaches, each based on different input features constructed by experts in the field. Then, the prediction model is trained using a machine-learning algorithm. Recently, with the development of convolutional neural networks (CNN), the training paradigm has changed. In computer vision, the expert-developed features have been significantly overpassed by automatically trained convolutional filters...
February 19, 2019: Bioinformatics
Tomi Pitkäaho, Aki Manninen, Thomas J Naughton
Deep artificial neural network learning is an emerging tool in image analysis. We demonstrate its potential in the field of digital holographic microscopy by addressing the challenging problem of determining the in-focus reconstruction depth of Madin-Darby canine kidney cell clusters encoded in digital holograms. A deep convolutional neural network learns the in-focus depths from half a million hologram amplitude images. The trained network correctly determines the in-focus depth of new holograms with high probability, without performing numerical propagation...
February 10, 2019: Applied Optics
Xiaohui Du, Lin Liu, Xiangzhou Wang, Guangming Ni, Jing Zhang, Ruqian Hao, Juanxiu Lin, Yong Liu
The analysis of fecal-type components for clinical diagnosis is important. The main examination involves the counting of red blood cells (RBCs), white blood cells (WBCs) and molds under the microscopic. With the development of machine vision, some vision-based detection schemes have been proposed. However, these methods have a single target for detection, with low detection efficiency and low accuracy. We proposed an algorithm to identify the visible image of fecal composition based on intelligent deep learning...
March 14, 2019: Bioscience Reports
Joshua J Podmore, Toby P Breckon, Nik K N Aznan, Jason D Connolly
Brain-computer interfaces (BCI) harnessing Steady State Visual Evoked Potentials (SSVEP) manipulate the frequency and phase of visual stimuli to generate predictable oscillations in neural activity. For BCI spellers, oscillations are matched with alphanumeric characters allowing users to select target numbers and letters. Advances in BCI spellers can, in part, be accredited to subject-speci?c optimization, including; 1) custom electrode arrangements, 2) ?lter sub-band assessments and 3) stimulus parameter tuning...
March 13, 2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Zhizhong Han, Honglei Lu, Zhenbao Liu, Chi-Man Vong, Yu-Shen Liua, Matthias Zwicker, Junwei Han, C L Philip Chen
Learning 3D global features by aggregating multiple views is important. Pooling is widely used to aggregate views in deep learning models. However, pooling disregards a lot of content information within views and the spatial relationship among the views, which limits the discriminability of learned features. To resolve this issue, 3D to Sequential Views (3D2SeqViews) is proposed to more effectively aggregate sequential views using convolutional neural networks with a novel hierarchical attention aggregation...
March 12, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Fantin Girard, Conrad Kavalec, Farida Cheriet
OBJECTIVE: Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation...
March 2019: Artificial Intelligence in Medicine
Haya Alaskar, Abir Hussain, Nourah Al-Aseem, Panos Liatsis, Dhiya Al-Jumeily
Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated system for detection and classification of ulcers in WCE images, based on state-of-the-art deep learning networks. Deep learning techniques, and in particular, convolutional neural networks (CNNs), have recently become popular in the analysis and recognition of medical images...
March 13, 2019: Sensors
Sumeet Shinde, Shweta Prasad, Yash Saboo, Rishabh Kaushick, Jitender Saini, Pramod Kumar Pal, Madhura Ingalhalikar
Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinson's disease (PD) as PD is characterized by loss of dopaminergic neurons in the SNc. Current techniques employ estimation of contrast ratios of the SNc, visualized on NMS-MRI, to discern PD patients from the healthy controls. However, the extraction of these features is time-consuming and laborious and moreover provides lower prediction accuracies. Furthermore, these do not account for patterns of subtle changes in PD in the SNc...
March 6, 2019: NeuroImage: Clinical
Bulat Ibragimov, Diego Toesca, Yixuan Yuan, Albert Koong, Chang Daniel, Lei Xing
Stereotactic body radiation therapy (SBRT) is a relatively novel treatment modality, with little post-treatment prognostic information reported. This study proposes a novel neural network-based paradigm for accurate prediction of liver SBRT outcomes. We assembled a database of patients treated with liver SBRT at our institution. Together with a 3D dose delivery plans for each SBRT treatment, other variables such as patients' demographics, quantified abdominal anatomy, history of liver comorbidities, other liver-directed therapies and liver function tests were collected...
March 11, 2019: IEEE Journal of Biomedical and Health Informatics
Sorour Mohajerani, Parvaneh Saeedi
Automatic identification of shadow regions in an image is a basic and yet very important task in many computer vision applications such as object detection, target tracking, and visual data analysis. Although shadow detection is a well-studied topic, current methods for identification of shadow are not as accurate as required. In this work, we propose a deep-learning method for shadow detection at a pixel-level that is suitable for single RGB images. The proposed CNN-based method benefits from a novel architecture through which global and local shadow attributes are identified using a new and efficient mapping scheme in the skip connection...
March 11, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Jeremias Sulam, Aviad Aberdam, Amir Beck, Michael Elad
Parsimonious representations are ubiquitous in modeling and processing information. Motivated by the recent Multi-Layer Convolutional Sparse Coding (ML-CSC) model, we herein generalize the traditional Basis Pursuit problem to a multi-layer setting, introducing similar sparse enforcing penalties at different representation layers in a symbiotic relation between synthesis and analysis sparse priors. We explore different iterative methods to solve this new problem in practice, and we propose a new Multi-Layer Iterative Soft Thresholding Algorithm (ML-ISTA), as well as a fast version (ML-FISTA)...
March 11, 2019: IEEE Transactions on Pattern Analysis and Machine Intelligence
Brian L DeCost, Bo Lei, Toby Francis, Elizabeth A Holm
We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively. We explore two microstructure segmentation tasks in an openly available ultrahigh carbon steel microstructure dataset: segmenting cementite particles in the spheroidized matrix, and segmenting larger fields of view featuring grain boundary carbide, spheroidized particle matrix, particle-free grain boundary denuded zone, and Widmanstätten cementite...
February 2019: Microscopy and Microanalysis
Hongxin Lin, Chao Wei, Guangxing Wang, Hu Chen, Lisheng Lin, Ming Ni, Jianxin Chen, Shuangmu Zhuo
In the case of hepatocellular carcinoma (HCC) samples, classification of differentiation is crucial for determining prognosis and treatment strategy decisions. However, a label-free and automated classification system for HCC grading has not been yet developed. Hence, in this study, we demonstrate the fusion of multiphoton microscopy and a deep-learning algorithm for classifying HCC differentiation to produce an innovative computer-aided diagnostic method. Convolutional neural networks based on the VGG-16 framework were trained using 217 combined two-photon excitation fluorescence and second-harmonic generation images; the resulting classification accuracy of the HCC differentiation grade was over 90%...
March 13, 2019: Journal of Biophotonics
Chun Fang, Yoshitaka Moriwaki, Aikui Tian, Caihong Li, Kentaro Shimizu
Molecular recognition features (MoRFs) are key functional regions of intrinsically disordered proteins (IDPs), which play important roles in the molecular interaction network of cells and are implicated in many serious human diseases. Identifying MoRFs is essential for both functional studies of IDPs and drug design. This study adopts the cutting-edge machine learning method of artificial intelligence to develop a powerful model for improving MoRFs prediction. We proposed a method, named as en_DCNNMoRF (ensemble deep convolutional neural network-based MoRF predictor)...
February 2019: Journal of Bioinformatics and Computational Biology
Nguyen Quoc Khanh Le, Quang-Thai Ho, Yu-Yen Ou
Deep learning has been increasingly and widely used to solve numerous problems in various fields with state-of-the-art performance. It can also be applied in bioinformatics to reduce the requirement for feature extraction and reach high performance. This study attempts to use deep learning to predict GTP binding sites in Rab proteins, which is one of the most vital molecular functions in life science. A functional loss of GTP binding sites in Rab proteins has been implicated in a variety of human diseases (choroideremia, intellectual disability, cancer, Parkinson's disease)...
February 2019: Journal of Bioinformatics and Computational Biology
Jing Han, Jian Yao, Jiao Zhao, Jingmin Tu, Yahui Liu
License plate detection (LPD) is the first and key step in license plate recognition. State-of-the-art object-detection algorithms based on deep learning provide a promising form of LPD. However, there still exist two main challenges. First, existing methods often enclose objects with horizontal rectangles. However, horizontal rectangles are not always suitable since license plates in images are multi-oriented, reflected by rotation and perspective distortion. Second, the scale of license plates often varies, leading to the difficulty of multi-scale detection...
March 7, 2019: Sensors
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