2,695 research outputs found
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN
Automatic detection of liver lesions in CT images poses a great challenge for
researchers. In this work we present a deep learning approach that models
explicitly the variability within the non-lesion class, based on prior
knowledge of the data, to support an automated lesion detection system. A
multi-class convolutional neural network (CNN) is proposed to categorize input
image patches into sub-categories of boundary and interior patches, the
decisions of which are fused to reach a binary lesion vs non-lesion decision.
For validation of our system, we use CT images of 132 livers and 498 lesions.
Our approach shows highly improved detection results that outperform the
state-of-the-art fully convolutional network. Automated computerized tools, as
shown in this work, have the potential in the future to support the
radiologists towards improved detection.Comment: To be presented at PatchMI: 3rd International Workshop on Patch-based
Techniques in Medical Imaging, MICCAI 201
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Recent advancement in Disease Diagnostic using machine learning: Systematic survey of decades, comparisons, and challenges
Computer-aided diagnosis (CAD), a vibrant medical imaging research field, is
expanding quickly. Because errors in medical diagnostic systems might lead to
seriously misleading medical treatments, major efforts have been made in recent
years to improve computer-aided diagnostics applications. The use of machine
learning in computer-aided diagnosis is crucial. A simple equation may result
in a false indication of items like organs. Therefore, learning from examples
is a vital component of pattern recognition. Pattern recognition and machine
learning in the biomedical area promise to increase the precision of disease
detection and diagnosis. They also support the decision-making process's
objectivity. Machine learning provides a practical method for creating elegant
and autonomous algorithms to analyze high-dimensional and multimodal
bio-medical data. This review article examines machine-learning algorithms for
detecting diseases, including hepatitis, diabetes, liver disease, dengue fever,
and heart disease. It draws attention to the collection of machine learning
techniques and algorithms employed in studying conditions and the ensuing
decision-making process
Cancer diagnosis using deep learning: A bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements
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