Handbook of Deep Learning in Biomedical Engineering: Techniques and Applications (Paperback)

1. Application of deep learning in biomedical engineering
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)