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Deep Learning for medical image processing

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https://read.qxmd.com/read/30742121/evaluation-and-accurate-diagnoses-of-pediatric-diseases-using-artificial-intelligence
#1
Huiying Liang, Brian Y Tsui, Hao Ni, Carolina C S Valentim, Sally L Baxter, Guangjian Liu, Wenjia Cai, Daniel S Kermany, Xin Sun, Jiancong Chen, Liya He, Jie Zhu, Pin Tian, Hua Shao, Lianghong Zheng, Rui Hou, Sierra Hewett, Gen Li, Ping Liang, Xuan Zang, Zhiqi Zhang, Liyan Pan, Huimin Cai, Rujuan Ling, Shuhua Li, Yongwang Cui, Shusheng Tang, Hong Ye, Xiaoyan Huang, Waner He, Wenqing Liang, Qing Zhang, Jianmin Jiang, Wei Yu, Jianqun Gao, Wanxing Ou, Yingmin Deng, Qiaozhen Hou, Bei Wang, Cuichan Yao, Yan Liang, Shu Zhang, Yaou Duan, Runze Zhang, Sarah Gibson, Charlotte L Zhang, Oulan Li, Edward D Zhang, Gabriel Karin, Nathan Nguyen, Xiaokang Wu, Cindy Wen, Jie Xu, Wenqin Xu, Bochu Wang, Winston Wang, Jing Li, Bianca Pizzato, Caroline Bao, Daoman Xiang, Wanting He, Suiqin He, Yugui Zhou, Weldon Haw, Michael Goldbaum, Adriana Tremoulet, Chun-Nan Hsu, Hannah Carter, Long Zhu, Kang Zhang, Huimin Xia
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs...
February 11, 2019: Nature Medicine
https://read.qxmd.com/read/30719560/spotting-malignancies-from-gastric-endoscopic-images-using-deep-learning
#2
Jang Hyung Lee, Young Jae Kim, Yoon Woo Kim, Sungjin Park, Youn-I Choi, Yoon Jae Kim, Dong Kyun Park, Kwang Gi Kim, Jun-Won Chung
BACKGROUND: Gastric cancer is a common kind of malignancies, with yearly occurrences exceeding one million worldwide in 2017. Typically, ulcerous and cancerous tissues develop abnormal morphologies through courses of progression. Endoscopy is a routinely adopted means for examination of gastrointestinal tract for malignancy. Early and timely detection of malignancy closely correlate with good prognosis. Repeated presentation of similar frames from gastrointestinal tract endoscopy often weakens attention for practitioners to result in true patients missed out to incur higher medical cost and unnecessary morbidity...
February 4, 2019: Surgical Endoscopy
https://read.qxmd.com/read/30686613/a-gentle-introduction-to-deep-learning-in-medical-image-processing
#3
Andreas Maier, Christopher Syben, Tobias Lasser, Christian Riess
This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains...
January 24, 2019: Zeitschrift Für Medizinische Physik
https://read.qxmd.com/read/30684090/rheumatoid-arthritis-atherosclerosis-imaging-and-cardiovascular-risk-assessment-using-machine-and-deep-learning-based-tissue-characterization
#4
REVIEW
Narendra N Khanna, Ankush D Jamthikar, Deep Gupta, Matteo Piga, Luca Saba, Carlo Carcassi, Argiris A Giannopoulos, Andrew Nicolaides, John R Laird, Harman S Suri, Sophie Mavrogeni, A D Protogerou, Petros Sfikakis, George D Kitas, Jasjit S Suri
PURPOSE OF THE REVIEW: Rheumatoid arthritis (RA) is a chronic, autoimmune disease which may result in a higher risk of cardiovascular (CV) events and stroke. Tissue characterization and risk stratification of patients with rheumatoid arthritis are a challenging problem. Risk stratification of RA patients using traditional risk factor-based calculators either underestimates or overestimates the CV risk. Advancements in medical imaging have facilitated early and accurate CV risk stratification compared to conventional cardiovascular risk calculators...
January 25, 2019: Current Atherosclerosis Reports
https://read.qxmd.com/read/30662564/artificial-intelligence-based-decision-making-for-age-related-macular-degeneration
#5
De-Kuang Hwang, Chih-Chien Hsu, Kao-Jung Chang, Daniel Chao, Chuan-Hu Sun, Ying-Chun Jheng, Aliaksandr A Yarmishyn, Jau-Ching Wu, Ching-Yao Tsai, Mong-Lien Wang, Chi-Hsien Peng, Ke-Hung Chien, Chung-Lan Kao, Tai-Chi Lin, Lin-Chung Woung, Shih-Jen Chen, Shih-Hwa Chiou
Artificial intelligence (AI) based on convolutional neural networks (CNNs) has a great potential to enhance medical workflow and improve health care quality. Of particular interest is practical implementation of such AI-based software as a cloud-based tool aimed for telemedicine, the practice of providing medical care from a distance using electronic interfaces. Methods: In this study, we used a dataset of labeled 35,900 optical coherence tomography (OCT) images obtained from age-related macular degeneration (AMD) patients and used them to train three types of CNNs to perform AMD diagnosis...
2019: Theranostics
https://read.qxmd.com/read/30661383/-potential-applications-of-deep-learning-based-technologies-in-hungarian-mammography
#6
Dezső Ribli, Richárd Zsuppán, Péter Pollner, Anna Horváth, Zoltán Bánsághi, István Csabai, Viktor Bérczi, Zsuzsa Unger
INTRODUCTION AND AIM: The technology, named 'deep learning' is the promising result of the last two decades of development in computer science. It poses an unavoidable challenge for medicine, how to understand, apply and adopt the - today not fully explored - possibilities that have become available by these new methods. METHOD: It is a gift and a mission, since the exponentially growing volume of raw data (from imaging, laboratory, therapy diagnostics or therapy interactions, etc...
January 2019: Orvosi Hetilap
https://read.qxmd.com/read/30637135/fine-grain-segmentation-of-the-intervertebral-discs-from-mr-spine-images-using-deep-convolutional-neural-networks-bsu-net
#7
Sewon Kim, Won C Bae, Koichi Masuda, Christine B Chung, Dosik Hwang
We propose a new deep learning network capable of successfully segmenting intervertebral discs and their complex boundaries from magnetic resonance (MR) spine images. The existing U-network (U-net) is known to perform well in various segmentation tasks in medical images; however, its performance with respect to details of segmentation such as boundaries is limited by the structural limitations of a max-pooling layer that plays a key role in feature extraction process in the U-net. We designed a modified convolutional and pooling layer scheme and applied a cascaded learning method to overcome these structural limitations of the max-pooling layer of a conventional U-net...
September 2018: Applied Sciences
https://read.qxmd.com/read/30617339/high-performance-medicine-the-convergence-of-human-and-artificial-intelligence
#8
REVIEW
Eric J Topol
The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health...
January 2019: Nature Medicine
https://read.qxmd.com/read/30617335/a-guide-to-deep-learning-in-healthcare
#9
REVIEW
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, Jeff Dean
Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed...
January 2019: Nature Medicine
https://read.qxmd.com/read/30596566/reliable-label-efficient-learning-for-biomedical-image-recognition
#10
Gu Yun, Mali Shen, Yang Jie, Guang-Zhong Yang
The use of deep neural networks for biomedical image analysis requires a sufficient number of labeled datasets. In order to acquire accurate labels as the gold standard, multiple clinicians with specific expertise are required for both annotation and proofreading. This process is time-consuming and labor-intensive, making high-quality and large-annotated biomedical datasets difficult. To address this problem, we propose a deep active learning framework which enables active selection of both informative queries and reliable experts...
December 27, 2018: IEEE Transactions on Bio-medical Engineering
https://read.qxmd.com/read/30579766/analysis-of-intensity-normalization-for-optimal-segmentation-performance-of-a-fully-convolutional-neural-network
#11
Nina Jacobsen, Andreas Deistung, Dagmar Timmann, Sophia L Goericke, Jürgen R Reichenbach, Daniel Güllmar
INTRODUCTION: Convolutional neural networks have begun to surpass classical statistical- and atlas based machine learning techniques in medical image segmentation in recent years, proving to be superior in performance and speed. However, a major challenge that the community faces are mismatch between variability within training and evaluation datasets and therefore a dependency on proper data pre-processing. Intensity normalization is a widely applied technique for reducing the variance of the data for which there are several methods available ranging from uniformity transformation to histogram equalization...
December 19, 2018: Zeitschrift Für Medizinische Physik
https://read.qxmd.com/read/30579222/a-deep-learning-framework-for-unsupervised-affine-and-deformable-image-registration
#12
Bob D de Vos, Floris F Berendsen, Max A Viergever, Hessam Sokooti, Marius Staring, Ivana Išgum
Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable image registration...
December 8, 2018: Medical Image Analysis
https://read.qxmd.com/read/30553609/an-overview-of-deep-learning-in-medical-imaging-focusing-on-mri
#13
Alexander Selvikvåg Lundervold, Arvid Lundervold
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry...
December 12, 2018: Zeitschrift Für Medizinische Physik
https://read.qxmd.com/read/30531869/cardiovascular-calcification-artificial-intelligence-and-big-data-accelerate-mechanistic-discovery
#14
REVIEW
Maximillian A Rogers, Elena Aikawa
Cardiovascular calcification is a health disorder with increasing prevalence and high morbidity and mortality. The only available therapeutic options for calcific vascular and valvular heart disease are invasive transcatheter procedures or surgeries that do not fully address the wide spectrum of these conditions; therefore, an urgent need exists for medical options. Cardiovascular calcification is an active process, which provides a potential opportunity for effective therapeutic targeting. Numerous biological processes are involved in calcific disease, including matrix remodelling, transcriptional regulation, mitochondrial dysfunction, oxidative stress, calcium and phosphate signalling, endoplasmic reticulum stress, lipid and mineral metabolism, autophagy, inflammation, apoptosis, loss of mineralization inhibition, impaired mineral resorption, cellular senescence and extracellular vesicles that act as precursors of microcalcification...
December 10, 2018: Nature Reviews. Cardiology
https://read.qxmd.com/read/30530377/cloud-deployment-of-high-resolution-medical-image-analysis-with-tomaat
#15
Fausto Milletari, Johann Frei, Moustafa Aboulatta, Gerome Vivar, Seyed-Ahmad Ahmadi
BACKGROUND: Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art, most of the methods cannot be easily accessed, compared or used by other researchers or clinicians. Even if developers publish their code and pre-trained models on the internet, integration in stand-alone applications and existing workflows is often not straightforward, especially for clinical research partners...
December 5, 2018: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30515089/deep-synthesis-of-realistic-medical-images-a-novel-tool-in-clinical-research-and-training
#16
Evgeniy Bart, Jay Hegdé
Making clinical decisions based on medical images is fundamentally an exercise in statistical decision-making. This is because in this case, the decision-maker must distinguish between image features that are clinically diagnostic (i.e., signal) from a large amount of non-diagnostic features. (i.e., noise). To perform this task, the decision-maker must have learned the underlying statistical distributions of the signal and noise to begin with. The same is true for machine learning algorithms that perform a given diagnostic task...
2018: Frontiers in Neuroinformatics
https://read.qxmd.com/read/30511660/visually-interpretable-deep-network-for-diagnosis-of-breast-masses-on-mammograms
#17
Seong Tae Kim, Jae-Hyeok Lee, Hakmin Lee, Yong Man Ro
Recently, deep learning technology has achieved various successes in medical image analysis studies including computer-aided diagnosis (CADx). However, current CADx approaches based on deep learning have a limitation in interpreting diagnostic decisions. The limited interpretability is a major challenge for practical use of current deep learning approaches. In this paper, a novel visually interpretable deep network framework is proposed to provide diagnostic decisions with visual interpretation. The proposed method is motivated by the fact that the radiologists characterize breast masses according to the breast imaging reporting and data system (BIRADS)...
December 4, 2018: Physics in Medicine and Biology
https://read.qxmd.com/read/30500819/deep-learning-for-lung-cancer-prognostication-a-retrospective-multi-cohort-radiomics-study
#18
Ahmed Hosny, Chintan Parmar, Thibaud P Coroller, Patrick Grossmann, Roman Zeleznik, Avnish Kumar, Johan Bussink, Robert J Gillies, Raymond H Mak, Hugo J W L Aerts
BACKGROUND: Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification. METHODS AND FINDINGS: We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68...
November 2018: PLoS Medicine
https://read.qxmd.com/read/30468663/state-of-the-art-review-on-deep-learning-in-medical-imaging
#19
Mainak Biswas, Venkatanareshbabu Kuppili, Luca Saba, Damodar Reddy Edla, Harman S Suri, Elisa Cuadrado-Godia, John R Laird, Rui Tato Marinhoe, Joao M Sanches, Andrew Nicolaides, Jasjit S Suri
Deep learning (DL) is affecting each and every sphere of public and private lives and becoming a tool for daily use. The power of DL lies in the fact that it tries to imitate the activities of neurons in the neocortex of human brain where the thought process takes place. Therefore, like the brain, it tries to learn and recognize patterns in the form of digital images. This power is built on the depth of many layers of computing neurons backed by high power processors and graphics processing units (GPUs) easily available today...
January 1, 2019: Frontiers in Bioscience (Landmark Edition)
https://read.qxmd.com/read/30449653/the-european-federation-of-organisations-for-medical-physics-efomp-white-paper-big-data-and-deep-learning-in-medical-imaging-and-in-relation-to-medical-physics-profession
#20
EDITORIAL
Mika Kortesniemi, Virginia Tsapaki, Annalisa Trianni, Paolo Russo, Ad Maas, Hans-Erik Källman, Marco Brambilla, John Damilakis
Big data and deep learning will profoundly change various areas of professions and research in the future. This will also happen in medicine and medical imaging in particular. As medical physicists, we should pursue beyond the concept of technical quality to extend our methodology and competence towards measuring and optimising the diagnostic value in terms of how it is connected to care outcome. Functional implementation of such methodology requires data processing utilities starting from data collection and management and culminating in the data analysis methods...
November 16, 2018: Physica Medica: PM
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