Journal ArticleDOI
Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images
Berkman Sahiner,Heang Ping Chan,Nicholas Petrick,Datong Wei,Mark A. Helvie,Dorit D. Adler,Mitchell M. Goodsitt +6 more
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TLDR
The authors' results demonstrate the feasibility of using a convolution neural network for classification of masses and normal tissue on mammograms using a generalized, fast and stable implementation of the CNN.Abstract:
The authors investigated the classification of regions of interest (ROI's) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a backpropagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained from the ROI's using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequently used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROIs containing biopsy-proven masses and 504 ROI's containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The authors' results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms.read more
Citations
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Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: 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, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
Journal ArticleDOI
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
TL;DR: The papers in this special section focus on the technology and applications supported by deep learning, which have proven to be powerful tools for a broad range of computer vision tasks.
Journal ArticleDOI
Large scale deep learning for computer aided detection of mammographic lesions
Thijs Kooi,Geert Litjens,Bram van Ginneken,Albert Gubern-Mérida,Clara I. Sánchez,Ritse M. Mann,Ard den Heeten,Nico Karssemeijer +7 more
TL;DR: A head‐to‐head comparison between a state‐of‐the art in mammography CAD system, relying on a manually designed feature set and a Convolutional Neural Network (CNN), aiming for a system that can ultimately read mammograms independently.
Journal ArticleDOI
Artificial intelligence in cancer imaging: Clinical challenges and applications.
Wenya Linda Bi,Ahmed Hosny,Matthew B. Schabath,Maryellen L. Giger,Nicolai Juul Birkbak,Nicolai Juul Birkbak,Alireza Mehrtash,Alireza Mehrtash,Tavis Allison,Tavis Allison,Omar Arnaout,Christopher Abbosh,Christopher Abbosh,Ian F. Dunn,Raymond H. Mak,Rulla M. Tamimi,Clare M. Tempany,Charles Swanton,Charles Swanton,Udo Hoffmann,Lawrence H. Schwartz,Lawrence H. Schwartz,Robert J. Gillies,Raymond Y. Huang,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +25 more
TL;DR: The authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types to illustrate how common clinical problems are being addressed.
Journal ArticleDOI
Overview of deep learning in medical imaging
Kenji Suzuki,Kenji Suzuki +1 more
TL;DR: It is shown that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deepLearning.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
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30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
Bernard Widrow,Michael A. Lehr +1 more
TL;DR: The history, origination, operating characteristics, and basic theory of several supervised neural-network training algorithms (including the perceptron rule, the least-mean-square algorithm, three Madaline rules, and the backpropagation technique) are described.
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Increased Rates of Convergence Through Learning Rate Adaptation
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ROC methodology in radiologic imaging
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J. S. Weszka,A. Rosenfeld +1 more
TL;DR: Three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively; it was found that the Fouriers generally performed more poorly, while the other feature sets all performned comparably.