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| 005 |
20210823091214.0 |
| 007 |
cr nn 008mamaa |
| 008 |
170713s2017 gw | s |||| 0|eng d |
| 020 |
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|a 9783319429991
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| 024 |
7 |
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|a 10.1007/978-3-319-42999-1
|2 doi
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| 040 |
|
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|a Sistema de Bibliotecas del Tecnológico de Costa Rica
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| 245 |
1 |
0 |
|a Deep Learning and Convolutional Neural Networks for Medical Image Computing :
|b Precision Medicine, High Performance and Large-Scale Datasets /
|c edited by Le Lu, Yefeng Zheng, Gustavo Carneiro, Lin Yang.
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| 250 |
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|a 1st ed. 2017.
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| 260 |
# |
# |
|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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| 300 |
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|a XIII, 326 p. 117 illus., 100 illus. in color. :
|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Advances in Computer Vision and Pattern Recognition,
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| 505 |
0 |
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|a Part 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.
|
| 650 |
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0 |
|a Optical data processing.
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| 650 |
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0 |
|a Artificial intelligence.
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| 650 |
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0 |
|a Neural networks (Computer science) .
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| 650 |
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0 |
|a Radiology.
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| 650 |
1 |
4 |
|a Image Processing and Computer Vision.
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| 650 |
2 |
4 |
|a Artificial Intelligence.
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| 650 |
2 |
4 |
|a Mathematical Models of Cognitive Processes and Neural Networks.
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| 650 |
2 |
4 |
|a Imaging / Radiology.
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| 700 |
1 |
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|a Lu, Le.
|e editor.
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| 700 |
1 |
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|a Zheng, Yefeng.
|e editor.
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| 700 |
1 |
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|a Carneiro, Gustavo.
|e editor.
|0 (orcid)0000-0002-5571-6220
|1 https://orcid.org/0000-0002-5571-6220
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| 700 |
1 |
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|a Yang, Lin.
|e editor.
|
| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
|