000796324 000__ 05898cam\a2200493K\\4500
000796324 001__ 796324
000796324 005__ 20230306143539.0
000796324 006__ m\\\\\o\\d\\\\\\\\
000796324 007__ cr\un\nnnunnun
000796324 008__ 170718s2017\\\\sz\\\\\\o\\\\\001\0\eng\d
000796324 019__ $$a993878230$$a994470032$$a999408065$$a1000377824
000796324 020__ $$a9783319429991$$q(electronic book)
000796324 020__ $$a331942999X$$q(electronic book)
000796324 020__ $$z9783319429984
000796324 020__ $$z3319429981
000796324 035__ $$aSP(OCoLC)ocn994053705
000796324 035__ $$aSP(OCoLC)994053705$$z(OCoLC)993878230$$z(OCoLC)994470032$$z(OCoLC)999408065$$z(OCoLC)1000377824
000796324 040__ $$aYDX$$beng$$cYDX$$dN$T$$dGW5XE$$dEBLCP$$dN$T$$dOCLCF$$dNJR$$dUAB
000796324 049__ $$aISEA
000796324 050_4 $$aQA76.87
000796324 08204 $$a006.3/2$$223
000796324 24500 $$aDeep learning and convolutional neural networks for medical image computing :$$bprecision medicine, high performance and large-scale datasets /$$cLe Lu, Yefeng Zheng, Gustavo Carneiro, Lin Yang, editors.
000796324 260__ $$aCham :$$bSpringer,$$c2017.
000796324 300__ $$a1 online resource.
000796324 336__ $$atext$$btxt$$2rdacontent
000796324 337__ $$acomputer$$bc$$2rdamedia
000796324 338__ $$aonline resource$$bcr$$2rdacarrier
000796324 4901_ $$aAdvances in computer vision and pattern recognition,$$x2191-6586
000796324 500__ $$aIncludes indexes.
000796324 5050_ $$aPart I: Review -- Chapter 1. Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective -- Chapter 2. Review of Deep Learning Methods in Mammography, Cardiovascular and Microscopy Image Analysis -- Part II: Detection and Localization -- Chapter 3. Efficient False-Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation -- Chapter 4. Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning -- Chapter 5. A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set -- Chapter 6. Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers -- Chapter 7. Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation, Unordered Pooling and Cross-Dataset Learning -- Chapter 8. Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging -- Chapter 9. Cell Detection with Deep Learning Accelerated by Sparse Kernel -- Chapter 10. Fully Convolutional Networks in Medical Imaging: Applications to Image Enhancement and Recognition -- Chapter 11. On the Necessity of Fine-Tuned Convolutional Neural Networks for Medical Imaging -- Part III: Segmentation -- Chapter 12. Fully Automated Segmentation Using Distance Regularized Level Set and Deep-Structured Learning and Inference -- Chapter 13. Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms -- Chapter 14. Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local vs. Global Image Context -- Chapter 15. Robust Cell Detection and Segmentation in Histopathological Images using Sparse Reconstruction and Stacked Denoising Autoencoders -- Chapter 16. Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling -- Part IV: Big Dataset and Text-Image Deep Mining -- Chapter 17. Interleaved Text/Image Deep Mining on a Large-Scale Radiology Image Database.
000796324 506__ $$aAccess limited to authorized users.
000796324 520__ $$aThis timely text/reference presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Topics and features: Highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing Discusses the insightful research experience and views of Dr. Ronald M. Summers in medical imaging-based computer-aided diagnosis and its interaction with deep learning Presents a comprehensive review of the latest research and literature on deep learning for medical image analysis Describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging Examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging Introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database for automated image interpretation This pioneering volume will prove invaluable to researchers and graduate students wishing to employ deep neural network models and representations for medical image analysis and medical imaging applications. Dr. Le Lu is a Staff Scientist in the Radiology and Imaging Sciences Department of the National Institutes of Health Clinical Center, Bethesda, MD, USA. Dr. Yefeng Zheng is a Senior Staff Scientist at Siemens Healthcare Technology Center, Princeton, NJ, USA. Dr. Gustavo Carneiro is an Associate Professor in the School of Computer Science at The University of Adelaide, Australia. Dr. Lin Yang is an Associate Professor in the Department of Biomedical Engineering at the University of Florida, Gainesville, FL, USA.
000796324 588__ $$aOnline resource; title from PDF title page (viewed July 31, 2017).
000796324 650_0 $$aNeural networks (Computer science)
000796324 650_0 $$aDiagnostic imaging$$xData processing.
000796324 7001_ $$aLu, Le,$$eeditor.
000796324 7001_ $$aZheng, Yefeng,$$eeditor.
000796324 7001_ $$aCarneiro, Gustavo,$$eeditor.
000796324 7001_ $$aYang, Lin,$$eeditor.
000796324 830_0 $$aAdvances in computer vision and pattern recognition.
000796324 852__ $$bebk
000796324 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-42999-1$$zOnline Access$$91397441.1
000796324 909CO $$ooai:library.usi.edu:796324$$pGLOBAL_SET
000796324 980__ $$aEBOOK
000796324 980__ $$aBIB
000796324 982__ $$aEbook
000796324 983__ $$aOnline
000796324 994__ $$a92$$bISE