Handbook of Deep Learning in Biomedical Engineering Techniques and Applications
Coordonnateurs : Emilia Balas Valentina, Mishra Brojo Kishore, Kumar Raghvendra
2. Applications, algorithms, tools directly related to deep learning
3. Computational Neuroscience; Neuroimaging and Time Series data (including MRI/fMRI/CT, EEG/MEG, etc.) studies;
4. Data Fusion for HealthCare, especially Biomedical images of different nature (X-ray, CT, etc.);
5. Deep neural network in medical image processing (RTG, USG, CT, PET, OCT and others)
6. Early diagnosis of specific diseases like Alzheimer, ADHD, ASD etc
7. Manifold learning, classification, clustering and regression in Neuroimaging data analysis;
8. Multimodal imaging techniques: data acquisition, reconstruction; 2D, 3D, 4D imaging, etc.)
9. Optimization by deep neural networks, Multi-dimensional deep learning
10. Prediction of tumor from MRI using deep learning
11. Theoretical understanding of deep learning in biomedical engineering
12. Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic analysis, quantitative measurements, image guidance of ultrasonography)
Dr. Brojo Kishore Mishra is currently working as a Professor in the Department of Computer Science and Engineering at the GIET University, Gunupur-765022, India. He received his PhD degree in Computer Science from the Berhampur University in 2012. He has published more than 30 research papers in national and international conference proceedings, 25 research papers in peer-reviewed journals, and 22 book chapters; authored 2 books; and edited 4 books. His research interests include data mining, machine learning, soft computing, and security. He has organized and co organized local and international conferences and also edited several special issues for journals. He is the Senior Member of IEEE and Life Member of CSI, ISTE. He is the Editor of CSI Journal of Computing.
Raghvendra Kumar is working as an Associate Professor in Computer Science and Engineering Department at GIET University, India. He received BTech, MTech, and PhD in Computer Science and Engineering, India, and Postdoc Fellow from the Institute of Information Technology, Virtual Reality and Multimedia, Vietnam. He has published a number of research papers in international journals and conferences. His research areas are computer networks, data mining, cloud computing, and secure multiparty computations, theory of computer scie
- Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT
- Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis
- Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks
- Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer’s, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography
Date de parution : 11-2020
Ouvrage de 320 p.
19x23.3 cm
Thèmes de Handbook of Deep Learning in Biomedical Engineering :
Mots-clés :
AI-assisted surgery; Artificial intelligence; Attacks; Biomedical engineering; Biomedical image and signal processing; Biomedical images; Blockchain in education; Brain; body; and machine interface; Breast cancer; CAD; CADx; Cancer community; CNN; Consensus; Convolutional neural network; CT; Decentralization; Deep learning; Depression detection; Early-stage diagnosis; Feature selection; Gene expression analysis; Genomic sequencing; Healthcare sector; Healthcare; Image classification; Image processing; Image segmentation; Imaging modalities; Immutability; Integrity; Machine learning; Medical domain; Medical image processing; Medical image; Medical images; Natural language processing; Neural network architecture; Neural network; Nonvisualization technique; Pathogen; Plant disease; Plant pathology; Precision agriculture; Public and medical health management system; Unsupervised feature learning; Visualization technique; Word embedding