Deep learning for medical image analysis:
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutio...
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| Weitere Verfasser: | , , |
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| Format: | Buch |
| Sprache: | English |
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London, United Kingdom
Academic Press, Elsevier
[2024]
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| Ausgabe: | Second edition |
| Schriftenreihe: | The Elsevier and Miccai Society book series
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| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis |
| Beschreibung: | 1. An Introduction to Neural Networks and Deep Learning; 2. Deep reinforcement learning in medical imaging; 3. CapsNet for medical image segmentation; 4.Transformer for Medical Image Analysis; 5. An overview of disentangled representation learning for MR images; 6. Hypergraph Learning and Its Applications for Medical Image Analysis; 7. Unsupervised Domain Adaptation for Medical Image Analysis; 8. Medical image synthesis and reconstruction using generative adversarial networks; 9. Deep Learning for Medical Image Reconstruction; 10. Dynamic inference using neural architecture search in medical image segmentation; 11. Multi-modality cardiac image analysis with deep learning; 12. Deep Learning-based Medical Image Registration; 13. Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI; 14. Deep Learning in Functional Brain Mapping and associated applications; 15. Detecting, Localising, and Classifying Polyps from Colonoscopy Videos Using Deep Learning; 16. OCTA Segmentation with limited training data using disentangled represenatation learning; 17. Considerations in the Assessment of Machine Learning Algorithm Performance for Medical Imaging |
| Beschreibung: | xxiii, 518 Seiten Illustrationen, Diagramme 235 mm |
| ISBN: | 9780323851244 |
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| 520 | |a Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis | ||
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Contents Contributors. XV Foreword .xxiii PART 1 Deep learning theories and architectures CHAPTER 1 An introduction to neural networks and deep learning . з Ahmad Wisnu Mulyadi, Jee Seok Yoon, Eunjin Jeon, Wonjun Ko, and Heung-Il Suk 1.1 1.2 1.3 1.4 1.5 1.6 1.7 Introduction. Feed-forward neural networks. 1.2.1 Perceptron . 1.2.2 Multi-layer perceptron . 1.2.3 Learning in feed-forward neural networks. Convolutional neural networks. 1.3.1 Convolution and pooling layer . 1.3.2 Computing gradients. 1.3.3 Deep convolutional neural networks. Recurrent neural networks. 1.4.1 Recurrent cell. 1.4.2 Vanishing gradient problem. Deep generative models. 1.5.1 Restricted Boltzmann machine
. 1.5.2 Deep belief network. 1.5.3 Deep Boltzmann machine. 1.5.4 Variational autoencoder. 1.5.5 Generative adversarial network. Tricks for better learning. 1.6.1 Parameter initialization in autoencoder . 1.6.2 Activation functions. 1.6.3 Optimizers . 1.6.4 Regularizations. 1.6.5 Normalizations. Open-source tools for deep learning. References. 3 3 3 5 6 7 8 9 9 11 11 13 13 13 14 15 16 18 20 20 21 23 25 26 28 28 CHAPTER 2 Deep reinforcement learning in medical imaging . зз S. Kevin Zhou and Qiyuan Wang 2.1 2.2 Introduction. Basics of reinforcement learning . 33 34 V
vi Contents 2.2.1 Markov decision process. 2.2.2 Model-free methods . 2.2.3 Model-based methods. 2.3 DRL in medical imaging. 2.3.1 DRL for parametric medical image analysis . 2.3.2 Solving optimization using DRL. 2.4 Future perspectives . 2.4.1 Challenges ahead . 2.4.2 The latest DRL advances . 2.5 Conclusions. References. 34 36 40 41 43 58 65 65 66 67 67 CapsNet for medical image segmentation. 75 CHAPTER 3 Minh Tran, Viet-Khoa Vo-Ho, Kyle Quinn, Hien Nguyen, Khoa Luu, and Ngan Le 3.1 3.2 3.3 3.4 Convolutional neural networks: limitations. Capsule network: fundamental. Capsule network: related work. CapsNets in medical image segmentation . 3.4.1 2D-SegCaps. 3.4.2 3D-SegCaps. 3.4.3 3D-UCaps
. 3.4.4 SS-3DCapsNet. 3.4.5 Comparison . Discussion. Acknowledgments. References. 75 76 80 81 81 85 87 89 91 93 95 95 CHAPTER 4 Transformer for medical image analysis. 99 3.5 Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, and Huazhu Fu 4.1 4.2 Introduction. Medical image segmentation. 4.2.1 Organ-specific segmentation. 4.2.2 Multi-organ segmentation. 4.3 Medical image classification . 4.3.1 COVID-19 diagnosis . 4.3.2 Tumor classification. 4.3.3 Retinal disease classification. 4.4 Medical image detection . 4.5 Medical image reconstruction . 4.5.1 Medical image
enhancement. 4.5.2 Medical image restoration. 99 99 100 103 107 108 Ill 112 112 113 113 114
Contents 4.6 Medical image synthesis . 4.6.1 Intra-modality approaches. 4.6.2 Inter-modality approaches. 4.7 Discussion and conclusion. References. 116 116 117 118 118 PART 2 Deep learning methods CHAPTER 5 An overview of disentangled representation learning for MR image harmonization . 135 Lianrui Zuo, Yihao Liu, Jerry L. Prince, and Aaron Carass 5.1 Introduction. 5.1.1 Domain shift. 5.1.2 Image-to-image translation and harmonization . 5.2 IIT and disentangled representation learning . 5.2.1 Supervised IIT and disentangling . 5.2.2 Unsupervised IIT and disentangling . 5.3 Unsupervised harmonization with supervised IIT. 5.3.1 The disentangling framework of CALAMITI . 5.3.2 Network architecture. 5.3.3 Domain adaptation. 5.3.4 Experiments and results . 5.4 Conclusions.
Acknowledgments. References. 135 135 138 139 140 142 143 143 145 145 147 149 149 150 CHAPTER 6 Hyper-graph learning and its applications for medical image analysis. 153 Yue Gao and Shuyi Ji 6.1 Introduction. 6.2 Preliminary of hyper-graph . 6.3 Hyper-graph neural networks. 6.3.1 Hyper-graph structure generation . 6.3.2 General hyper-graph neural networks . 6.3.3 Dynamic hyper-graph neural networks . 6.3.4 Hyper-graph learning toolbox. 6.4 Hyper-graph learning for medical image analysis. 6.5 Application 1: hyper-graph learning for COVID-19 identification using CT images. 164 6.5.1 Method. 6.5.2 Experiments. 6.6 Application 2: hyper-graph learning for survival prediction on whole slides histopathological images . 153 154 155 156 156 159 162 163 165 169 173 vii
viii Contents Ranking-based survival prediction on histopathological whole-slide images . 174 6.6.2 Big hyper-graph factorization neural network for survival prediction from whole slide image. 180 6.7 Conclusions. 181 References. 181 6.6.1 CHAPTER 7 Unsupervised domain adaptation for medical image analysis. 185 Yuexiang Li, Luyan Liu, Cheng Bian, Kai Ma, and Yefeng Zheng 7.1 Introduction. 7.2 Image space alignment. 7.2.1 MI2GAN . 7.2.2 Implementation details. 7.2.3 Experiments. 7.3 Feature space alignment. 7.3.1 Uncertainty-aware feature space domain adaptation . 7.4 Experiments . 7.4.1 Exploration on uncertainty estimation. 7.4.2 Comparison with existing UDA frameworks. 7.5 Output space alignment. 7.5.1 Robust cross-denoising network . 7.5.2
Experiments. 7.6 Conclusion . References. 185 188 189 191 192 196 197 202 204 206 208 209 212 215 216 PART 3 Medical image reconstruction and synthesis CHAPTER 8 Medical image synthesis and reconstruction using generative adversarial networks. 225 Gyutaek Oh and Jong Chui Ye 8.1 Introduction. 225 8.2 Types of GAN. 226 8.2.1 GAN . 226 8.2.2 Conditional GAN. 227 8.2.3 AmbientGAN. 227 8.2.4 Least squares GAN and Wasserstein GAN . 228 8.2.5 Cycle-consistent GAN . 229 8.2.6 Optimal transport driven CycleGAN. 230 8.2.7 StarGAN . 231 8.2.8 Collaborative GAN. 232 8.3 Applications of GAN for medical imaging. 234 8.3.1 Multi-contrast MR image synthesis using cGAN . 234
Contents MRI reconstruction without fully-sampled data using AmbientGAN. 237 8.3.3 Low dose CT denoising using CycleGAN. 8.3.4 MRI reconstruction without paired data using OT-CycleGAN. 240 8.3.5 MR contrast imputation using CollaGAN. 8.4 Summary. References. 8.3.2 238 243 244 244 CHAPTER 9 Deep learning for medical image reconstruction . 247 Jun Zhao, Qiu Huang, Dong Liang, Yang Chen, and Ge Wang 9.1 9.2 Introduction. 247 Deep learning for MRI reconstruction . 247 9.2.1 Introduction. 247 9.2.2 Basic of MR reconstruction. 248 9.2.3 Deep learning MRI reconstruction withsupervised learning . 249 9.2.4 Deep learning MRI reconstruction with unsupervised learning . 253 9.2.5 Outlook . 255 9.2.6 Conclusion. 256 9.3 Deep learning for CT reconstruction . 256 9.3.1 Image domain post-processing
. 257 9.3.2 Hybrid domain-based processing. 259 9.3.3 Iterative reconstruction via deep learning . 261 9.3.4 Direct reconstruction via deep learning. 262 9.3.5 Conclusion. 263 9.4 Deep learning for PET reconstruction . 264 9.4.1 Introduction . 264 9.4.2 Conventional PET reconstruction . 265 9.4.3 Deep learning-based algorithms in PETimaging . 266 9.4.4 Conclusion. 268 9.5 Discussion and conclusion. 268 References. 269 PART 4 Medical image segmentation, registration, and applications CHAPTER 10 Dynamic inference using neural architecture search in medical image segmentation. 281 Dong Yang, Holger R. Roth, Xiaosong Wang, Ziyue Xu, and Daguang Xu ix
X Contents 10.1 10.2 10.3 10.4 10.5 10.6 10.7 Introduction . 281 Related works.283 10.2.1 Efficient ConvNet models for medical imaging. 283 10.2.2 Domain adaptation. 283 10.2.3 Neural architecture search.284 Data oriented medical image segmentation . 285 10.3.1 Super-net design and training . 285 10.3.2 Data adaptation with super-net . 288 Experiments . 290 Ablation study. 292 10.5.1 Validation with single path or multiple paths. 292 10.5.2 Guided search and random search. 293 10.5.3 Training with single path or multiple paths .293 Additional experiments .294 Discussions. 294 References. 295 CHAPTER 11 Multi-modality cardiac image analysis with deep learning . 299 Lei Li, Fuping Wu, Sihang
Wang, and Xiahai Zhuang 11.1 Introduction. 299 11.2 Multi-sequence cardiac MRI based myocardial and pathology segmentation. 299 11.2.1 Introduction . 299 11.2.2 Methodology summary for challenge events . 300 11.2.3 Data and results . 303 11.2.4 Discussion and conclusion . 306 11.3 LGE MRI based left atrial scar segmentation and quantification. 306 11.3.1 Introduction. 306 11.3.2 Method. 307 11.3.3 Data and results . 311 11.3.4 Conclusion and future work . 315 11.4 Domain adaptation for cross-modality cardiac image segmentation. 316 11.4.1 Introduction. 316 11.4.2 Method. 318 11.4.3 Data and results . 325 11.4.4
Conclusion. 330 References. 331 CHAPTER 12 Deep learning-based medical image registration . 337 Xiaohuan Cao, Peng Xue, Jingfan Fan, Dingkun Liu, Kaicong Sun, Zhong Xue, and Dinggang Shen
Contents 12.1 12.2 12.3 12.4 Introduction. Deep learning-based medical image registration methods . 12.2.1 Deep learning-based medical image registration: supervised learning. 339 12.2.2 Deep learning-based medical image registration: unsupervised learning. 342 12.2.3 Deep learning-based medical image registration: weakly-supervised learning. 344 12.2.4 Deep learning-based registration: smoothness, consistency and other properties. 345 Deep learning-based registration withsemantic information . Concluding remarks. References. 337 339 349 352 352 CHAPTER 13 Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI. 357 James Duncan, Lawrence H. Staib, Nicha Dvornek, Xiaoxiao Li, Juntang Zhuang, Jiyao Wang, and Pamela Ventola 13.1 13.2 13.3 13.4 Introduction. BrainGNN. 13.2.1 Notation. 13.2.2 Architecture overview. 13.2.3 ROI-aware graph convolutional layer
. 13.2.4 ROI-topK pooling layer . 13.2.5 Readout layer. 13.2.6 Putting layers together . 13.2.7 Loss functions . 13.2.8 Experiments and results . 13.2.9 Brain-GNN implication for dynamic brain states . LSTM-based recurrent neural networksfor prediction in ASD 13.3.1 Basic LSTM architecture for task-based fMRI. 13.3.2 Strategies for learning from small data sets. 13.3.3 Prediction of treatment outcome. Causality and effective connectivity in ASD. 13.4.1 Dynamic causal modeling. 13.4.2 The effective connectome. 13.4.3 Overcoming long time series and noise with multiple shooting model driven learning (MS-MDL). 377 13.4.4 Adjoint state method. 13.4.5 Multiple-shooting adjoint state method (MSA) . 13.4.6 Validation of MSA on toy examples . 13.4.7 Application to large-scale systems. 357 358 359 359 362 363 364 364 364 366 370 371 371 372 373 374 376 376 379 381 382 382 xi
xii Contents 13.5 13.4.8 Apply MDL to identify ASD from fMRI data. 13.4.9 Improved fitting with АСА and . 13.4.10 Estimation of effective connectome and functional connectome . 386 13.4.11 Classification results for task fMRI. Conclusion . References. 385 386 389 389 390 CHAPTER 14 Deep learning in functional brain mapping and associated applications. 395 Ning Qiang, Qinglin Dong, Heng Huang, Han Wang, Shijie Zhao, Xintao Hu, Qing Li, Wei Zhang, Yiheng Liu, Mengshen He, Bao Ge, Lin Zhao, Zihao Wu, Lu Zhang, Steven Xu, Dajiang Zhu, Xi Jiang, and Tianming Liu 14.1 14.2 14.3 14.4 14.5 14.6 Introduction. 395 Deep learning models for mapping functional brain networks . 397 14.2.1 Convolutional auto-encoder (CAE). 397 14.2.2 Recurrent neural network (RNN) . 399 14.2.3 Deep belief network (DBN) . 402 14.2.4 Variational auto-encoder (VAE).404 14.2.5 Generative adversarial net (GAN). 406 Spatio-temporal models of fMRI. 408 14.3.1 Deep sparse recurrent auto-encoder (DSRAE). 408
14.3.2 Spatio-temporal attention auto-encoder (STAAE) . . 409 14.3.3 Multi-head guided attention graph neural networks (multi-head GAGNNs). 411 14.3.4 SCAAE and STCA.412 Neural architecture search (NAS) of deep learning models on fMRI. 413 14.4.1 Hybrid spatio-temporal neural architecture search net (HS-NASNet). 414 14.4.2 Deep belief network with neural architecture search (NAS-DBN). 415 14.4.3 eNAS-DSRAE. 415 14.4.4 ST-DARTS. 416 Representing brain function as embedding. 417 14.5.1 Hierarchical interpretable autoencoder (HIAE). 418 14.5.2 Temporally correlated autoencoder (TCAE) . 418 14.5.3 Potential applications . 419 Deep fusion of brain structure-function in brain disorders . 419 14.6.1 Deep cross-model attention network (DCMAT).420 14.6.2 Deep connectome. 420
Contents 14.7 Conclusion . 421 References. 421 CHAPTER 15 Detecting, localizing and classifying polyps from colonoscopy videos using deep learning . 425 Yu Tian, Leonardo Zorron Cheng Tao Pu, Yuyuan Liu, Gabriel Maicas, Johan W. Verjans, Alastair D. Burt, Seon Ho Shin, Rajvinder Singh, and Gustavo Carneiro 15.1 Introduction. 425 15.2 Literature review. 427 15.2.1 Polyp detection. 427 15.2.2 Polyp localization and classification . 428 15.2.3 Uncertainty and calibration. 428 15.2.4 Commercial systems. 429 15.3 Materials and methods. 429 15.3.1 Datasets. 429 15.3.2 Methods. 432 15.4 Results and discussion. 437 15.4.1 Polyp detection experiments. 438 15.4.2 Polyp localization and classification experiments . 441 15.4.3 Uncertainty estimation and calibration experiments . 442 15.4.4 System running
time. 445 15.5 Conclusion . 446 References. 446 CHAPTER 16 OCTA segmentation with limited training data using disentangled representation learning. 451 Yihao Liu, Lianrui Zuo, Yufan He, Shuo Han, Jianqin Lei, Jerry L. Prince, and Aaron Carass 16.1 Introduction. 451 16.2 Related work. 454 16.3 Method. 455 16.3.1 Overview . 455 16.3.2 Conditional variational auto-encoder. 456 16.3.3 Anatomy-contrast disentanglement. 457 16.3.4 Semi-supervised segmentation . 459 16.3.5 Data sets and manual delineations. 459 16.3.6 Foveal avascular zone segmentation . 462 16.4 Discussion and conclusion. 463 References. 466 xiii
χίν Contents PART 5 Others_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ CHAPTER 17 Considerations in the assessment of machine learning algorithm performance for medical imaging. 473 Alexej Gossmann, Berkman Sahiner, Ravi K. Samala, Si Wen, Kenny H. Cha, and Nicholas Petrick 17.1 17.2 17.3 17.4 17.5 17.6 17.7 Introduction. 473 17.1.1 Medical devices, software as a medical device and intended use. 475 Datasets. 477 17.2.1 General principles. 477 17.2.2 Independence of training and test data sets . 478 17.2.3 Reference standard. 479 17.2.4 Image collection and fairness . 479 17.2.5 Image and data quality . 480 17.2.6 Discussion .481 Endpoints . 481 17.3.1 Metrics. 482 Study design. 485 17.4.1 Transportability
.485 17.4.2 Assessment studies for ML algorithms in medical imaging .486 17.4.3 Discussion .492 Bias. 492 17.5.1 Bias and precision. 493 17.5.2 Bias and generalizability. 494 17.5.3 Types and sources of bias in pre-deployment performance evaluation studies of ML algorithms in medical imaging. 494 17.5.4 Discussion . 498 Limitations and future considerations. 498 Conclusion . 500 References. 501 Index . 509 |
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An Introduction to Neural Networks and Deep Learning; 2. Deep reinforcement learning in medical imaging; 3. CapsNet for medical image segmentation; 4.Transformer for Medical Image Analysis; 5. An overview of disentangled representation learning for MR images; 6. Hypergraph Learning and Its Applications for Medical Image Analysis; 7. Unsupervised Domain Adaptation for Medical Image Analysis; 8. Medical image synthesis and reconstruction using generative adversarial networks; 9. Deep Learning for Medical Image Reconstruction; 10. Dynamic inference using neural architecture search in medical image segmentation; 11. Multi-modality cardiac image analysis with deep learning; 12. Deep Learning-based Medical Image Registration; 13. Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI; 14. Deep Learning in Functional Brain Mapping and associated applications; 15. Detecting, Localising, and Classifying Polyps from Colonoscopy Videos Using Deep Learning; 16. OCTA Segmentation with limited training data using disentangled represenatation learning; 17. Considerations in the Assessment of Machine Learning Algorithm Performance for Medical Imaging</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Bildgebendes Verfahren</subfield><subfield code="0">(DE-588)4006617-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Cloud Computing</subfield><subfield code="0">(DE-588)7623494-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Bildanalyse</subfield><subfield code="0">(DE-588)4145391-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Klein- und Mittelbetrieb</subfield><subfield code="0">(DE-588)4031031-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4143413-4</subfield><subfield code="a">Aufsatzsammlung</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Cloud Computing</subfield><subfield code="0">(DE-588)7623494-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Klein- und Mittelbetrieb</subfield><subfield code="0">(DE-588)4031031-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Bildgebendes Verfahren</subfield><subfield code="0">(DE-588)4006617-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Bildanalyse</subfield><subfield code="0">(DE-588)4145391-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="4"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, S. Kevin</subfield><subfield code="d">ca. 20./21. Jh.</subfield><subfield code="0">(DE-588)1334324565</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Greenspan, Hayit</subfield><subfield code="d">ca. 20./21. Jh.</subfield><subfield code="0">(DE-588)1334324883</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shen, Dinggang</subfield><subfield code="0">(DE-588)1131560000</subfield><subfield code="4">edt</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Passau - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034740696&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034740696</subfield></datafield></record></collection> |
| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV049413691 |
| illustrated | Illustrated |
| index_date | 2024-07-03T23:06:36Z |
| indexdate | 2024-11-11T09:03:43Z |
| institution | BVB |
| isbn | 9780323851244 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034740696 |
| oclc_num | 1418690861 |
| open_access_boolean | |
| owner | DE-29T DE-739 |
| owner_facet | DE-29T DE-739 |
| physical | xxiii, 518 Seiten Illustrationen, Diagramme 235 mm |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Academic Press, Elsevier |
| record_format | marc |
| series2 | The Elsevier and Miccai Society book series |
| spelling | Deep learning for medical image analysis edited by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen Second edition London, United Kingdom Academic Press, Elsevier [2024] xxiii, 518 Seiten Illustrationen, Diagramme 235 mm txt rdacontent n rdamedia nc rdacarrier The Elsevier and Miccai Society book series 1. An Introduction to Neural Networks and Deep Learning; 2. Deep reinforcement learning in medical imaging; 3. CapsNet for medical image segmentation; 4.Transformer for Medical Image Analysis; 5. An overview of disentangled representation learning for MR images; 6. Hypergraph Learning and Its Applications for Medical Image Analysis; 7. Unsupervised Domain Adaptation for Medical Image Analysis; 8. Medical image synthesis and reconstruction using generative adversarial networks; 9. Deep Learning for Medical Image Reconstruction; 10. Dynamic inference using neural architecture search in medical image segmentation; 11. Multi-modality cardiac image analysis with deep learning; 12. Deep Learning-based Medical Image Registration; 13. Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI; 14. Deep Learning in Functional Brain Mapping and associated applications; 15. Detecting, Localising, and Classifying Polyps from Colonoscopy Videos Using Deep Learning; 16. OCTA Segmentation with limited training data using disentangled represenatation learning; 17. Considerations in the Assessment of Machine Learning Algorithm Performance for Medical Imaging Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis Bildgebendes Verfahren (DE-588)4006617-4 gnd rswk-swf Cloud Computing (DE-588)7623494-0 gnd rswk-swf Bildanalyse (DE-588)4145391-8 gnd rswk-swf Klein- und Mittelbetrieb (DE-588)4031031-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Cloud Computing (DE-588)7623494-0 s Klein- und Mittelbetrieb (DE-588)4031031-0 s Bildgebendes Verfahren (DE-588)4006617-4 s Bildanalyse (DE-588)4145391-8 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Zhou, S. Kevin ca. 20./21. Jh. (DE-588)1334324565 edt Greenspan, Hayit ca. 20./21. Jh. (DE-588)1334324883 edt Shen, Dinggang (DE-588)1131560000 edt Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034740696&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Deep learning for medical image analysis Bildgebendes Verfahren (DE-588)4006617-4 gnd Cloud Computing (DE-588)7623494-0 gnd Bildanalyse (DE-588)4145391-8 gnd Klein- und Mittelbetrieb (DE-588)4031031-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4006617-4 (DE-588)7623494-0 (DE-588)4145391-8 (DE-588)4031031-0 (DE-588)4193754-5 (DE-588)4143413-4 |
| title | Deep learning for medical image analysis |
| title_auth | Deep learning for medical image analysis |
| title_exact_search | Deep learning for medical image analysis |
| title_exact_search_txtP | Deep learning for medical image analysis |
| title_full | Deep learning for medical image analysis edited by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen |
| title_fullStr | Deep learning for medical image analysis edited by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen |
| title_full_unstemmed | Deep learning for medical image analysis edited by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen |
| title_short | Deep learning for medical image analysis |
| title_sort | deep learning for medical image analysis |
| topic | Bildgebendes Verfahren (DE-588)4006617-4 gnd Cloud Computing (DE-588)7623494-0 gnd Bildanalyse (DE-588)4145391-8 gnd Klein- und Mittelbetrieb (DE-588)4031031-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Bildgebendes Verfahren Cloud Computing Bildanalyse Klein- und Mittelbetrieb Maschinelles Lernen Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034740696&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT zhouskevin deeplearningformedicalimageanalysis AT greenspanhayit deeplearningformedicalimageanalysis AT shendinggang deeplearningformedicalimageanalysis |