16,128 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
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
To Learn or Not to Learn Features for Deformable Registration?
Feature-based registration has been popular with a variety of features
ranging from voxel intensity to Self-Similarity Context (SSC). In this paper,
we examine the question on how features learnt using various Deep Learning (DL)
frameworks can be used for deformable registration and whether this feature
learning is necessary or not. We investigate the use of features learned by
different DL methods in the current state-of-the-art discrete registration
framework and analyze its performance on 2 publicly available datasets. We draw
insights into the type of DL framework useful for feature learning and the
impact, if any, of the complexity of different DL models and brain parcellation
methods on the performance of discrete registration. Our results indicate that
the registration performance with DL features and SSC are comparable and stable
across datasets whereas this does not hold for low level features.Comment: 9 pages, 4 figure
Deeply-Supervised CNN for Prostate Segmentation
Prostate segmentation from Magnetic Resonance (MR) images plays an important
role in image guided interven- tion. However, the lack of clear boundary
specifically at the apex and base, and huge variation of shape and texture
between the images from different patients make the task very challenging. To
overcome these problems, in this paper, we propose a deeply supervised
convolutional neural network (CNN) utilizing the convolutional information to
accurately segment the prostate from MR images. The proposed model can
effectively detect the prostate region with additional deeply supervised layers
compared with other approaches. Since some information will be abandoned after
convolution, it is necessary to pass the features extracted from early stages
to later stages. The experimental results show that significant segmentation
accuracy improvement has been achieved by our proposed method compared to other
reported approaches.Comment: Due to a crucial sign error in equation
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