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Deep Learning for medical image processing

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
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
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
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
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
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)
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
Mitsuaki Ishioka, Toshiaki Hirasawa, Tomohiro Tada
Early detection of gastric cancer is one of the most important factors for improving the prognosis of patients. However, the detection rate for gastric cancer differs depending on the endoscopist's experience. In the last decade, new deep-learning-based machine learning methods have shown significant improvements in image recognition and have therefore been applied to various medical fields. Previously, we have reported the efficacy of our deep learning-based convolutional neural network (CNN) system for detecting gastric cancer in still images...
November 18, 2018: Digestive Endoscopy: Official Journal of the Japan Gastroenterological Endoscopy Society
Tian Xia, Ashnil Kumar, Dagan Feng, Jinman Kim
Tumor histopathology is a crucial step in cancer diagnosis which involves visual inspection of imaging data to detect the presence of tumor cells among healthy tissues. This manual process can be time-consuming, error-prone, and influenced by the expertise of the pathologist. Recent deep learning methods for image classification and detection using convolutional neural networks (CNNs) have demonstrated marked improvements in the accuracy of a variety of medical imaging analysis tasks. However, most well-established deep learning methods require large annotated training datasets that are specific to the particular problem domain; such datasets are difficult to acquire for histopathology data where visual characteristics differ between different tissue types, in addition to the need for precise annotations...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Max Blendowski, Mattias P Heinrich
PURPOSE: Deep convolutional neural networks in their various forms are currently achieving or outperforming state-of-the-art results on several medical imaging tasks. We aim to make these developments available to the so far unsolved task of accurate correspondence finding-especially with regard to image registration. METHODS: We propose a two-step hybrid approach to make deep learned features accessible to a discrete optimization-based registration method. In a first step, in order to extract expressive binary local descriptors, we train a deep network architecture on a patch-based landmark retrieval problem as auxiliary task...
November 14, 2018: International Journal of Computer Assisted Radiology and Surgery
Kai Zhang, Xiyang Liu, Fan Liu, Lin He, Lei Zhang, Yahan Yang, Wangting Li, Shuai Wang, Lin Liu, Zhenzhen Liu, Xiaohang Wu, Haotian Lin
BACKGROUND: Although artificial intelligence performs promisingly in medicine, few automatic disease diagnosis platforms can clearly explain why a specific medical decision is made. OBJECTIVE: We aimed to devise and develop an interpretable and expandable diagnosis framework for automatically diagnosing multiple ocular diseases and providing treatment recommendations for the particular illness of a specific patient. METHODS: As the diagnosis of ocular diseases highly depends on observing medical images, we chose ophthalmic images as research material...
November 14, 2018: Journal of Medical Internet Research
Aria Pezeshk, Sardar Hamidian, Nicholas Petrick, Berkman Sahiner
Deep 2D convolutional neural networks (CNNs) have been remarkably successful in producing record-breaking results in a variety of computer vision tasks. It is possible to extend CNNs to three dimensions using 3D kernels to make them suitable for volumetric medical imaging data such as CT or MRI, but this increases the processing time as well as the required number of training samples (due to the higher number of parameters that need to be learned). In this work, we address both of these issues for a 3D CNN implementation through the development of a two-stage computer-aided detection system for automatic detection of pulmonary nodules...
November 9, 2018: IEEE Journal of Biomedical and Health Informatics
Matthew Adams, Weijia Chen, David Holcdorf, Mark W McCusker, Piers Dl Howe, Frank Gaillard
INTRODUCTION: To evaluate the accuracy of deep convolutional neural networks (DCNNs) for detecting neck of femur (NoF) fractures on radiographs, in comparison with perceptual training in medically-naïve individuals. METHODS: This study extends a previous study that conducted perceptual training in medically-naïve individuals for the detection of NoF fractures on a variety of dataset sizes. The same anteroposterior hip radiograph dataset was used to train two DCNNs (AlexNet and GoogLeNet) to detect NoF fractures...
November 8, 2018: Journal of Medical Imaging and Radiation Oncology
Mahendra Khened, Varghese Alex Kollerathu, Ganapathy Krishnamurthi
Deep fully convolutional neural network (FCN) based architectures have shown great potential in medical image segmentation. However, such architectures usually have millions of parameters and inadequate number of training samples leading to over-fitting and poor generalization. In this paper, we present a novel DenseNet based FCN architecture for cardiac segmentation which is parameter and memory efficient. We propose a novel up-sampling path which incorporates long skip and short-cut connections to overcome the feature map explosion in conventional FCN based architectures...
January 2019: Medical Image Analysis
Claudia Mazo, Jose Bernal, Maria Trujillo, Enrique Alegre
BACKGROUND AND OBJECTIVE: Automatic classification of healthy tissues and organs based on histology images is an open problem, mainly due to the lack of automated tools. Solutions in this regard have potential in educational medicine and medical practices. Some preliminary advances have been made using image processing techniques and classical supervised learning. Due to the breakthrough performance of deep learning in various areas, we present an approach to recognise and classify, automatically, fundamental tissues and organs using Convolutional Neural Networks (CNN)...
October 2018: Computer Methods and Programs in Biomedicine
Hyoung Suk Park, Sung Min Lee, Hwa Pyung Kim, Jin Keun Seo, Yong Eun Chung
PURPOSE: This paper proposes a sinogram-consistency learning method to deal with beam-hardening related artifacts in polychromatic computerized tomography (CT). The presence of highly attenuating materials in the scan field causes an inconsistent sinogram, that does not match the range space of the Radon transform. When the mismatched data are entered into the range space during CT reconstruction, streaking and shading artifacts are generated owing to the inherent nature of the inverse Radon transform METHODS: The proposed learning method aims to repair inconsistent sinogram by removing the primary metal-induced beam-hardening factors along the metal trace in the sinogram...
September 20, 2018: Medical Physics
Sebastien Jean Mambou, Petra Maresova, Ondrej Krejcar, Ali Selamat, Kamil Kuca
Women's breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis...
August 25, 2018: Sensors
Seyed Sadegh Mohseni Salehi, Shadab Khan, Deniz Erdogmus, Ali Gholipour
With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-totemplate 3D rigid registration, we propose deep learning-based methods that are trained to find the 3D position of arbitrarily oriented subjects or anatomy in a canonical space based on slices or volumes of medical images. For this, we propose regression convolutional neural networks (CNNs) that learn to predict the angle-axis representation of 3D rotations and translations using image features...
August 21, 2018: IEEE Transactions on Medical Imaging
Bihan Wen, Saiprasad Ravishankar, Yoram Bresler
Techniques exploiting the sparsity of images in a transform domain are effective for various applications in image and video processing. In particular, transform learning methods involve cheap computations and have been demonstrated to perform well in applications such as image denoising and medical image reconstruction. Recently, we proposed methods for online learning of sparsifying transforms from streaming signals, which enjoy good convergence guarantees, and involve lower computational costs than online synthesis dictionary learning...
August 16, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Natalie Stephenson, Emily Shane, Jessica Chase, Jason Rowland, David Ries, Nicola Justice, Jie Zhang, Leong Chan, Renzhi Cao
Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still an expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in various fields, such as speech recognition, image classification, bioinformatics, etc...
August 19, 2018: Current Drug Metabolism
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