8 best deep learning medical imaging for 2022
Finding your suitable deep learning medical imaging is not easy. You may need consider between hundred or thousand products from many store. In this article, we make a short list of the best deep learning medical imaging including detail information and customer reviews. Let’s find out which is your favorite one.
1. Deep Learning for Medical Image Analysis
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Academic PressDescription
Deep learning is providing exciting solutions for medical image analysis problems and is seen as 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 have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas.
Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.
- Covers common research problems in medical image analysis and their challenges
- Describes deep learning methods and the theories behind approaches for medical image analysis
- Teaches how algorithms are applied to a broad range of application areas, includingChest X-ray, breast CAD, lung and chest, microscopy and pathology, etc.
- Includes a Forewordwritten byNicholas Ayache
2. Deep Learning and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets (Advances in Computer Vision and Pattern Recognition)
Description
This book 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. 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 of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; 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.
3. Machine Learning and Medical Imaging (Elsevier and Micca Society)
Description
Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs.
The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.
- Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems
- Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics
- Features self-contained chapters with a thorough literature review
- Assesses the development of future machine learning techniques and the further application of existing techniques
4. Guide to Medical Image Analysis: Methods and Algorithms (Advances in Computer Vision and Pattern Recognition)
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SpringerDescription
NA5. Medical Image Analysis
Description
The expanded and revised edition will split Chapter 4 to includemore details and examples in FMRI, DTI, and DWI for MR imagemodalities. The book will also expand ultrasound imaging to 3-Ddynamic contrast ultrasound imaging in a separate chapter.A new chapter on Optical Imaging Modalities elaboratingmicroscopy, confocal microscopy, endoscopy, optical coherenttomography, fluorescence and molecular imaging will be added.Another new chapter on Simultaneous Multi-Modality Medical Imagingincluding CT-SPECT and CT-PET will also be added. In the imageanalysis part, chapters on image reconstructions and visualizationswill be significantly enhanced to include, respectively, 3-D faststatistical estimation based reconstruction methods, and 3-D imagefusion and visualization overlaying multi-modality imaging andinformation. A new chapter on Computer-Aided Diagnosis and imageguided surgery, and surgical and therapeutic intervention will alsobe added.
A companion site containing power point slides, authorbiography, corrections to the first edition and images from thetext can be found here:
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6. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
Description
7. Machine Learning in Bio-Signal Analysis and Diagnostic Imaging
Description
Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented.
The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers.
- Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging
- Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining
- Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains
8. Machine Learning in Medical Imaging: 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 5, 2015, Proceedings (Lecture Notes in Computer Science)
Description
This book constitutes the proceedings of the 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015, held in conjunction with MICCAI 2015, in Munich in October 2015.
The 40 full papers presented in this volume were carefully reviewed and selected from 69 submissions. The workshop focuses on major trends and challenges in the area of machine learning in medical imaging and present works aimed to identify new cutting-edge techniques and their use in medical imaging.
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