Deep Learning in Medical Image Analysis
The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big d...
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| Year of Publication: | 2021 |
| Language: | English |
| Physical Description: | 1 electronic resource (458 p.) |
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| 100 | 1 | |a Zhang, Yudong |4 edt | |
| 245 | 1 | 0 | |a Deep Learning in Medical Image Analysis |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (458 p.) | ||
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| 520 | |a The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis. | ||
| 546 | |a English | ||
| 653 | |a interpretable/explainable machine learning | ||
| 653 | |a image classification | ||
| 653 | |a image processing | ||
| 653 | |a machine learning models | ||
| 653 | |a white box | ||
| 653 | |a black box | ||
| 653 | |a cancer prediction | ||
| 653 | |a deep learning | ||
| 653 | |a multimodal learning | ||
| 653 | |a convolutional neural networks | ||
| 653 | |a autism | ||
| 653 | |a fMRI | ||
| 653 | |a texture analysis | ||
| 653 | |a melanoma | ||
| 653 | |a glcm matrix | ||
| 653 | |a machine learning | ||
| 653 | |a classifiers | ||
| 653 | |a explainability | ||
| 653 | |a explainable AI | ||
| 653 | |a XAI | ||
| 653 | |a medical imaging | ||
| 653 | |a diagnosis | ||
| 653 | |a ARMD | ||
| 653 | |a change detection | ||
| 653 | |a unsupervised learning | ||
| 653 | |a microwave breast imaging | ||
| 653 | |a image reconstruction | ||
| 653 | |a tumor detection | ||
| 653 | |a digital pathology | ||
| 653 | |a whole slide image processing | ||
| 653 | |a multiple instance learning | ||
| 653 | |a deep learning classification | ||
| 653 | |a HER2 | ||
| 653 | |a medical images | ||
| 653 | |a transfer learning | ||
| 653 | |a optimizers | ||
| 653 | |a neo-adjuvant treatment | ||
| 653 | |a tumour cellularity | ||
| 653 | |a cancer | ||
| 653 | |a breast cancer | ||
| 653 | |a diagnostics | ||
| 653 | |a imaging | ||
| 653 | |a computation | ||
| 653 | |a artificial intelligence | ||
| 653 | |a 3D segmentation | ||
| 653 | |a active surface | ||
| 653 | |a discriminant analysis | ||
| 653 | |a PET imaging | ||
| 653 | |a medical image analysis | ||
| 653 | |a brain tumor | ||
| 653 | |a cervical cancer | ||
| 653 | |a colon cancer | ||
| 653 | |a lung cancer | ||
| 653 | |a computer vision | ||
| 653 | |a musculoskeletal images | ||
| 653 | |a lung disease detection | ||
| 653 | |a taxonomy | ||
| 653 | |a convolutional neural network | ||
| 653 | |a CycleGAN | ||
| 653 | |a data augmentation | ||
| 653 | |a dermoscopic images | ||
| 653 | |a domain transfer | ||
| 653 | |a macroscopic images | ||
| 653 | |a skin lesion segmentation | ||
| 653 | |a infection detection | ||
| 653 | |a COVID-19 | ||
| 653 | |a X-ray images | ||
| 653 | |a bayesian inference | ||
| 653 | |a shifted-scaled dirichlet distribution | ||
| 653 | |a MCMC | ||
| 653 | |a gibbs sampling | ||
| 653 | |a object detection | ||
| 653 | |a surgical tools | ||
| 653 | |a open surgery | ||
| 653 | |a egocentric camera | ||
| 653 | |a computers in medicine | ||
| 653 | |a segmentation | ||
| 653 | |a MRI | ||
| 653 | |a ECG signal detection | ||
| 653 | |a portable monitoring devices | ||
| 653 | |a 1D-convolutional neural network | ||
| 653 | |a medical image segmentation | ||
| 653 | |a domain adaptation | ||
| 653 | |a meta-learning | ||
| 653 | |a U-Net | ||
| 653 | |a computed tomography (CT) | ||
| 653 | |a magnetic resonance imaging (MRI) | ||
| 653 | |a low-dose | ||
| 653 | |a sparse-angle | ||
| 653 | |a quantitative comparison | ||
| 776 | |z 3-0365-1469-4 | ||
| 776 | |z 3-0365-1470-8 | ||
| 700 | 1 | |a Gorriz, Juan Manuel |4 edt | |
| 700 | 1 | |a Dong, Zhengchao |4 edt | |
| 700 | 1 | |a Zhang, Yudong |4 oth | |
| 700 | 1 | |a Gorriz, Juan Manuel |4 oth | |
| 700 | 1 | |a Dong, Zhengchao |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:39:52 Europe/Vienna |f system |c marc21 |a 2022-04-04 09:22:53 Europe/Vienna |g false | ||
| AVE | |i DOAB Directory of Open Access Books |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338114660004498&Force_direct=true |Z 5338114660004498 |b Available |8 5338114660004498 | ||